From 9fdc7d3f2b75309b700554a507d6aecce0b35e93 Mon Sep 17 00:00:00 2001
From: "Matthew W. Thompson" <mattwthompson@protonmail.com>
Date: Fri, 25 Sep 2026 09:07:57 -0500
Subject: [PATCH 01/20] Stage Jacobian app

---
 tyff/compute/_jacobian.py | 50 +++++++++++++++++++++++++++++++++++++++
 tyff/compute/apps.py      |  9 +++++++
 2 files changed, 59 insertions(+)
 create mode 100644 tyff/compute/_jacobian.py

diff --git a/tyff/compute/_jacobian.py b/tyff/compute/_jacobian.py
new file mode 100644
index 0000000..54ba5af
--- /dev/null
+++ b/tyff/compute/_jacobian.py
@@ -0,0 +1,50 @@
+"""Compute the ensemble average(s) and Jacobian matrix associated with a job."""
+
+import pathlib
+
+import torch
+
+import tyff
+
+
+def ensemble_average_jacobian(
+    system: tyff.TensorSystem,
+    frames_path: pathlib.Path,
+    temperature: float,
+    pressure: float | None,
+    force_field: tyff.TensorForceField,
+) -> tuple[dict[str, torch.Tensor], torch.Tensor]:
+    import openmm.unit
+    import torch
+
+    import tyff.mm
+    from tyff.mm._ops import _pack_force_field, _unpack_force_field
+
+    # Use existing tyff packing order, including attributes and optional v-sites.
+    tensors, parameter_lookup, attribute_lookup, has_v_sites = _pack_force_field(force_field)
+    parameters = torch.cat([t.detach().reshape(-1) for t in tensors if t is not None]).requires_grad_(True)
+    pieces = iter(parameters.split([t.numel() for t in tensors if t is not None]))
+    tensors = tuple(None if t is None else next(pieces).reshape(t.shape) for t in tensors)
+    worker_ff = _unpack_force_field(
+        tensors,
+        parameter_lookup,
+        attribute_lookup,
+        has_v_sites,
+        force_field,
+    )
+    means, _ = tyff.mm.compute_ensemble_averages(
+        system,
+        worker_ff,
+        frames_path,
+        temperature * openmm.unit.kelvin,
+        None if pressure is None else pressure * openmm.unit.atmosphere,
+    )
+
+    # Jacobian rows follow sorted observable names; columns follow packed parameters.
+    jacobian = torch.stack(
+        [
+            torch.autograd.grad(means[name], parameters, retain_graph=True)[0]
+            for name in sorted(means)  # Claude doesn't think this should be sorted
+        ]
+    )
+    return {name: value.detach() for name, value in means.items()}, jacobian.detach()
diff --git a/tyff/compute/apps.py b/tyff/compute/apps.py
index 2ba95c3..af03bcc 100644
--- a/tyff/compute/apps.py
+++ b/tyff/compute/apps.py
@@ -83,3 +83,12 @@ def run_dhvap_analysis(
     from tyff.compute._analyze import _run_dhvap_analysis
 
     return _run_dhvap_analysis(job_dirs)
+
+
+@python_app
+def create_jacobian(
+    job_dir: str,
+) -> dict[str, float]:
+    from tyff.compute._jacobian import _create_jacobian
+
+    return _create_jacobian(job_dir)

From 5f07be2f2d5f8859560a7ca89ebbb24bfd79710b Mon Sep 17 00:00:00 2001
From: "Matthew W. Thompson" <mattwthompson@protonmail.com>
Date: Fri, 25 Sep 2026 12:41:46 -0500
Subject: [PATCH 02/20] Remove temporary analysis app

---
 tyff/_tests/compute/test_analysis.py          | 23 -------
 .../app_files/make_sample_density_files.py    |  9 ---
 tyff/compute/_analyze.py                      | 62 -------------------
 tyff/compute/apps.py                          | 20 ------
 4 files changed, 114 deletions(-)
 delete mode 100644 tyff/_tests/compute/test_analysis.py
 delete mode 100644 tyff/compute/_analyze.py

diff --git a/tyff/_tests/compute/test_analysis.py b/tyff/_tests/compute/test_analysis.py
deleted file mode 100644
index 141c02b..0000000
--- a/tyff/_tests/compute/test_analysis.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import shutil
-from importlib.resources import files
-
-from tyff.compute._analyze import _run_density_analysis
-
-
-def test_basic_analysis(tmp_path):
-    # shim - see comment in source code
-    for file in ["production.csv"]:
-        shutil.copy(
-            str(files("tyff") / f"_tests/data/app_files/sample_density/{file}"),
-            str(tmp_path / file),
-        )
-
-    analysis_result = _run_density_analysis(
-        job_dir=str(tmp_path),
-    )
-
-    # just some non-zero number
-    assert analysis_result["mean"] > 0.0
-
-    # std should be <10% of the mean
-    assert analysis_result["std"] / analysis_result["mean"] < 0.1
diff --git a/tyff/_tests/data/app_files/make_sample_density_files.py b/tyff/_tests/data/app_files/make_sample_density_files.py
index bc2c6ec..9344fcc 100644
--- a/tyff/_tests/data/app_files/make_sample_density_files.py
+++ b/tyff/_tests/data/app_files/make_sample_density_files.py
@@ -2,9 +2,6 @@
 import pathlib
 import random
 
-from rich.pretty import pprint as print
-
-from tyff.compute._analyze import _run_density_analysis
 from tyff.compute._equilibrate import _run_equilibration
 from tyff.compute._minimize import _minimize_energy
 from tyff.compute._pack import _prepare_packed_topology
@@ -70,9 +67,3 @@
     equilibration_future=equilibration_result,
     job_dir="sample_density",
 )
-
-analysis_result = _run_density_analysis(
-    job_dir="sample_density",
-)
-
-print(analysis_result)
diff --git a/tyff/compute/_analyze.py b/tyff/compute/_analyze.py
deleted file mode 100644
index aaa0fa6..0000000
--- a/tyff/compute/_analyze.py
+++ /dev/null
@@ -1,62 +0,0 @@
-from __future__ import annotations
-
-import json
-
-import pandas
-
-
-def _run_density_analysis(
-    job_dir: str,
-) -> dict[str, float]:
-    """Run a naive density analysis from recorded simulation data."""
-    from tyff.compute._logging import _set_up_logger
-
-    logger = _set_up_logger(f"{job_dir}/analyze.log")
-
-    logger.info("Starting density analysis")
-
-    # TODO: Double check for equilibration/stability
-    try:
-        dataframe = pandas.read_csv(f"{job_dir}/production.csv")
-    except TypeError:
-        logger.error(f"Production run CSV data file production.csv not found, we are in {job_dir=}")
-        raise
-
-    mean = dataframe["Density (g/mL)"].mean()
-    std = dataframe["Density (g/mL)"].std()
-
-    logger.info(f"Estimated mean density: {mean:.4f} +/- {std:.4f} g/mL")
-
-    return {
-        "mean": mean,
-        "std": std,
-    }
-
-
-def _run_dhvap_analysis(
-    job_dirs: list[str],
-) -> dict[str, float]:
-    from openff.units import unit
-
-    configs = {job_dir: json.load(open(f"{job_dir}/compute_config.json")) for job_dir in job_dirs}
-
-    gas_dir = next(dir for dir, config in configs.items() if config["tag"] == "gas")
-    liquid_dir = next(dir for dir, config in configs.items() if config["tag"] == "liquid")
-
-    gas_temperature = configs[gas_dir]["temperature"]
-    liquid_temperature = configs[liquid_dir]["temperature"]
-
-    n_molecules = configs[liquid_dir]["n_molecules"]
-
-    assert gas_temperature == liquid_temperature, "Gas and liquid temperatures must be the same for dHvap calculation"
-
-    e_gas = pandas.read_csv(f"{gas_dir}/production.csv")["Potential Energy (kJ/mole)"].mean()
-    e_liquid = pandas.read_csv(f"{liquid_dir}/production.csv")["Potential Energy (kJ/mole)"].mean()
-
-    R = (1 * unit.avogadro_number * unit.boltzmann_constant).m_as("kJ/K")
-
-    dhvap = e_gas - e_liquid / n_molecules + R * gas_temperature  # kJ/mol
-
-    return {
-        "dhvap": dhvap,
-    }
diff --git a/tyff/compute/apps.py b/tyff/compute/apps.py
index af03bcc..7669f62 100644
--- a/tyff/compute/apps.py
+++ b/tyff/compute/apps.py
@@ -65,26 +65,6 @@ def run_production(
     return _run_production(production_config, equilibration_future, job_dir)
 
 
-@python_app
-def run_density_analysis(
-    job_dir: str,
-) -> dict[str, float]:
-    """Run a naive density analysis of production trajectories. For debugging only, not for tensor fitting."""
-    from tyff.compute._analyze import _run_density_analysis
-
-    return _run_density_analysis(job_dir)
-
-
-@python_app
-def run_dhvap_analysis(
-    job_dirs: list[str],
-) -> dict[str, float]:
-    """Run a naive DHVAP analysis of production trajectories. For debugging only, not for tensor fitting."""
-    from tyff.compute._analyze import _run_dhvap_analysis
-
-    return _run_dhvap_analysis(job_dirs)
-
-
 @python_app
 def create_jacobian(
     job_dir: str,

From 738e63779daeb05f84684374b87fc12981203bcf Mon Sep 17 00:00:00 2001
From: "Matthew W. Thompson" <mattwthompson@protonmail.com>
Date: Fri, 25 Sep 2026 13:07:59 -0500
Subject: [PATCH 03/20] Refactor app call, load some values from disk

---
 tyff/compute/_jacobian.py | 23 +++++++++++++++++------
 tyff/compute/apps.py      |  4 ++--
 2 files changed, 19 insertions(+), 8 deletions(-)

diff --git a/tyff/compute/_jacobian.py b/tyff/compute/_jacobian.py
index 54ba5af..a0d01de 100644
--- a/tyff/compute/_jacobian.py
+++ b/tyff/compute/_jacobian.py
@@ -7,18 +7,29 @@
 import tyff
 
 
-def ensemble_average_jacobian(
-    system: tyff.TensorSystem,
-    frames_path: pathlib.Path,
-    temperature: float,
-    pressure: float | None,
-    force_field: tyff.TensorForceField,
+def _get_ensemble_average_and_jacobian(
+    job_dir: str,
 ) -> tuple[dict[str, torch.Tensor], torch.Tensor]:
     import openmm.unit
     import torch
 
     import tyff.mm
     from tyff.mm._ops import _pack_force_field, _unpack_force_field
+    # the arguments we really care about are:
+    #     system: tyff.TensorSystem,
+    #     frames_path: pathlib.Path,
+    #     temperature: float,
+    #     pressure: float | None,
+    #     force_field: tyff.TensorForceField,
+    #
+    # so grab them from scattered files we expect to be in this job directory.
+
+    compute_config = BulkLiquid(**json.load(open(f"{job_dir}/compute_config.json")))  # type: ignore[typeddict-item]
+
+    temperature = compute_config["temperature"]  # kelvin, float
+    pressure = compute_config.get("pressure")  # atmosphere, float | None
+
+    frames_path = f"{job_dir}/production_trajectory.msgpack"
 
     # Use existing tyff packing order, including attributes and optional v-sites.
     tensors, parameter_lookup, attribute_lookup, has_v_sites = _pack_force_field(force_field)
diff --git a/tyff/compute/apps.py b/tyff/compute/apps.py
index 7669f62..98cf8a2 100644
--- a/tyff/compute/apps.py
+++ b/tyff/compute/apps.py
@@ -69,6 +69,6 @@ def run_production(
 def create_jacobian(
     job_dir: str,
 ) -> dict[str, float]:
-    from tyff.compute._jacobian import _create_jacobian
+    from tyff.compute._jacobian import _get_ensemble_average_and_jacobian
 
-    return _create_jacobian(job_dir)
+    return _get_ensemble_average_and_jacobian(job_dir)

From 4b5fa2046489a73134ade0342f70a710a92be563 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Fri, 25 Sep 2026 18:08:58 +0000
Subject: [PATCH 04/20] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 tyff/compute/_jacobian.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/tyff/compute/_jacobian.py b/tyff/compute/_jacobian.py
index a0d01de..ff25c16 100644
--- a/tyff/compute/_jacobian.py
+++ b/tyff/compute/_jacobian.py
@@ -1,11 +1,7 @@
 """Compute the ensemble average(s) and Jacobian matrix associated with a job."""
 
-import pathlib
-
 import torch
 
-import tyff
-
 
 def _get_ensemble_average_and_jacobian(
     job_dir: str,

From 797293422b7f2bb2100dea79223d9db62d3ce326 Mon Sep 17 00:00:00 2001
From: "Matthew W. Thompson" <mattwthompson@protonmail.com>
Date: Fri, 25 Sep 2026 13:47:46 -0500
Subject: [PATCH 05/20] More input management

---
 tyff/compute/_jacobian.py | 35 +++++++++++++++++++++++++++++++----
 tyff/compute/_prepare.py  | 11 +++++++++++
 2 files changed, 42 insertions(+), 4 deletions(-)

diff --git a/tyff/compute/_jacobian.py b/tyff/compute/_jacobian.py
index a0d01de..6c3c9ad 100644
--- a/tyff/compute/_jacobian.py
+++ b/tyff/compute/_jacobian.py
@@ -1,10 +1,13 @@
 """Compute the ensemble average(s) and Jacobian matrix associated with a job."""
 
+import glob
+import json
 import pathlib
 
 import torch
+from openff.toolkit import Interchange
 
-import tyff
+from tyff.configs.liquid import BulkLiquid
 
 
 def _get_ensemble_average_and_jacobian(
@@ -24,15 +27,39 @@ def _get_ensemble_average_and_jacobian(
     #
     # so grab them from scattered files we expect to be in this job directory.
 
+    # TODO: Handle case of cas simulations
     compute_config = BulkLiquid(**json.load(open(f"{job_dir}/compute_config.json")))  # type: ignore[typeddict-item]
 
     temperature = compute_config["temperature"]  # kelvin, float
     pressure = compute_config.get("pressure")  # atmosphere, float | None
 
-    frames_path = f"{job_dir}/production_trajectory.msgpack"
+    interchanges = []
+    for path_index, interchange_path in enumerate(glob.glob(f"{job_dir}/single_molecule_interchange_*.json")):
+        unique_molecule_index = int(pathlib.Path(interchange_path).stem.split("single_molecule_interchange_")[-1])
+
+        # hope we're loading up the single-molecule interchanges in the same order as we have unique molecules
+        assert unique_molecule_index == path_index
+
+        with open(interchange_path) as f:
+            interchanges.append(Interchange.model_validate_json(f.read()))
+
+    tensor_force_field, tensor_topologies = tyff.converters.convert_interchange(interchanges)
+
+    # must sync this up with tyff/compute/_pack.py if ever either change
+    n_molecules = compute_config["n_molecules"]
+    # also hope ordering lines up
+    n_copies = [int(n_molecules * x) for x in compute_config["x"]]
+
+    system = tyff.TensorSystem(
+        topologies=tensor_topologies,
+        n_copies=n_copies,
+        is_periodic=True,  # need to handle the case of gas simulations
+    )
+
+    frames_path = pathlib.Path(f"{job_dir}/production_trajectory.msgpack")
 
     # Use existing tyff packing order, including attributes and optional v-sites.
-    tensors, parameter_lookup, attribute_lookup, has_v_sites = _pack_force_field(force_field)
+    tensors, parameter_lookup, attribute_lookup, has_v_sites = _pack_force_field(tensor_force_field)
     parameters = torch.cat([t.detach().reshape(-1) for t in tensors if t is not None]).requires_grad_(True)
     pieces = iter(parameters.split([t.numel() for t in tensors if t is not None]))
     tensors = tuple(None if t is None else next(pieces).reshape(t.shape) for t in tensors)
@@ -41,7 +68,7 @@ def _get_ensemble_average_and_jacobian(
         parameter_lookup,
         attribute_lookup,
         has_v_sites,
-        force_field,
+        tensor_force_field,
     )
     means, _ = tyff.mm.compute_ensemble_averages(
         system,
diff --git a/tyff/compute/_prepare.py b/tyff/compute/_prepare.py
index 5d08cc7..2bbda64 100644
--- a/tyff/compute/_prepare.py
+++ b/tyff/compute/_prepare.py
@@ -48,6 +48,17 @@ def _prepare_openmm_system(
 
     interchange = force_field.create_interchange(packed_topology)
 
+    # later analysis depends on making `TensorSystem`s from `TensoTopology`s which may prefer
+    # to be constructed from single-molecule `Interchange`s, so just save them out here
+    #
+    # ordering is quite fragile, but hope order of smiles list never changes mid-job
+    for unique_molecule_index, unique_molecule in enumerate(
+        [Molecule.from_smiles(smiles) for smiles in compute_config["smiles"]]
+    ):
+        single_molecule_interchange = force_field.create_interchange(unique_molecule.to_topology())
+        with open(f"{job_dir}/single_molecule_interchange_{unique_molecule_index}.json", "w") as f:
+            f.write(single_molecule_interchange.model_dump_json())
+
     with open(files["interchange"].filepath, "w") as f:
         f.write(interchange.model_dump_json())
 

From de28e608615ee257dfe1696843f376959169e479 Mon Sep 17 00:00:00 2001
From: "Matthew W. Thompson" <mattwthompson@protonmail.com>
Date: Fri, 25 Sep 2026 13:53:22 -0500
Subject: [PATCH 06/20] Fix import, add temporary check

---
 tyff/compute/_jacobian.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tyff/compute/_jacobian.py b/tyff/compute/_jacobian.py
index 6c3c9ad..cff228d 100644
--- a/tyff/compute/_jacobian.py
+++ b/tyff/compute/_jacobian.py
@@ -5,7 +5,7 @@
 import pathlib
 
 import torch
-from openff.toolkit import Interchange
+from openff.interchange import Interchange
 
 from tyff.configs.liquid import BulkLiquid
 
@@ -60,6 +60,9 @@ def _get_ensemble_average_and_jacobian(
 
     # Use existing tyff packing order, including attributes and optional v-sites.
     tensors, parameter_lookup, attribute_lookup, has_v_sites = _pack_force_field(tensor_force_field)
+
+    assert tensors is not None and len(tensors) > 0
+
     parameters = torch.cat([t.detach().reshape(-1) for t in tensors if t is not None]).requires_grad_(True)
     pieces = iter(parameters.split([t.numel() for t in tensors if t is not None]))
     tensors = tuple(None if t is None else next(pieces).reshape(t.shape) for t in tensors)

From fd311f192906a3c5090a9645f79ccedab5d7d7ba Mon Sep 17 00:00:00 2001
From: "Matthew W. Thompson" <mattwthompson@protonmail.com>
Date: Mon, 28 Sep 2026 12:12:58 -0500
Subject: [PATCH 07/20] Revert "Remove temporary analysis app"

This reverts commit 5f07be2f2d5f8859560a7ca89ebbb24bfd79710b.
---
 tyff/_tests/compute/test_analysis.py          | 23 +++++++
 .../app_files/make_sample_density_files.py    |  9 +++
 tyff/compute/_analyze.py                      | 62 +++++++++++++++++++
 tyff/compute/apps.py                          | 20 ++++++
 4 files changed, 114 insertions(+)
 create mode 100644 tyff/_tests/compute/test_analysis.py
 create mode 100644 tyff/compute/_analyze.py

diff --git a/tyff/_tests/compute/test_analysis.py b/tyff/_tests/compute/test_analysis.py
new file mode 100644
index 0000000..141c02b
--- /dev/null
+++ b/tyff/_tests/compute/test_analysis.py
@@ -0,0 +1,23 @@
+import shutil
+from importlib.resources import files
+
+from tyff.compute._analyze import _run_density_analysis
+
+
+def test_basic_analysis(tmp_path):
+    # shim - see comment in source code
+    for file in ["production.csv"]:
+        shutil.copy(
+            str(files("tyff") / f"_tests/data/app_files/sample_density/{file}"),
+            str(tmp_path / file),
+        )
+
+    analysis_result = _run_density_analysis(
+        job_dir=str(tmp_path),
+    )
+
+    # just some non-zero number
+    assert analysis_result["mean"] > 0.0
+
+    # std should be <10% of the mean
+    assert analysis_result["std"] / analysis_result["mean"] < 0.1
diff --git a/tyff/_tests/data/app_files/make_sample_density_files.py b/tyff/_tests/data/app_files/make_sample_density_files.py
index 9344fcc..bc2c6ec 100644
--- a/tyff/_tests/data/app_files/make_sample_density_files.py
+++ b/tyff/_tests/data/app_files/make_sample_density_files.py
@@ -2,6 +2,9 @@
 import pathlib
 import random
 
+from rich.pretty import pprint as print
+
+from tyff.compute._analyze import _run_density_analysis
 from tyff.compute._equilibrate import _run_equilibration
 from tyff.compute._minimize import _minimize_energy
 from tyff.compute._pack import _prepare_packed_topology
@@ -67,3 +70,9 @@
     equilibration_future=equilibration_result,
     job_dir="sample_density",
 )
+
+analysis_result = _run_density_analysis(
+    job_dir="sample_density",
+)
+
+print(analysis_result)
diff --git a/tyff/compute/_analyze.py b/tyff/compute/_analyze.py
new file mode 100644
index 0000000..aaa0fa6
--- /dev/null
+++ b/tyff/compute/_analyze.py
@@ -0,0 +1,62 @@
+from __future__ import annotations
+
+import json
+
+import pandas
+
+
+def _run_density_analysis(
+    job_dir: str,
+) -> dict[str, float]:
+    """Run a naive density analysis from recorded simulation data."""
+    from tyff.compute._logging import _set_up_logger
+
+    logger = _set_up_logger(f"{job_dir}/analyze.log")
+
+    logger.info("Starting density analysis")
+
+    # TODO: Double check for equilibration/stability
+    try:
+        dataframe = pandas.read_csv(f"{job_dir}/production.csv")
+    except TypeError:
+        logger.error(f"Production run CSV data file production.csv not found, we are in {job_dir=}")
+        raise
+
+    mean = dataframe["Density (g/mL)"].mean()
+    std = dataframe["Density (g/mL)"].std()
+
+    logger.info(f"Estimated mean density: {mean:.4f} +/- {std:.4f} g/mL")
+
+    return {
+        "mean": mean,
+        "std": std,
+    }
+
+
+def _run_dhvap_analysis(
+    job_dirs: list[str],
+) -> dict[str, float]:
+    from openff.units import unit
+
+    configs = {job_dir: json.load(open(f"{job_dir}/compute_config.json")) for job_dir in job_dirs}
+
+    gas_dir = next(dir for dir, config in configs.items() if config["tag"] == "gas")
+    liquid_dir = next(dir for dir, config in configs.items() if config["tag"] == "liquid")
+
+    gas_temperature = configs[gas_dir]["temperature"]
+    liquid_temperature = configs[liquid_dir]["temperature"]
+
+    n_molecules = configs[liquid_dir]["n_molecules"]
+
+    assert gas_temperature == liquid_temperature, "Gas and liquid temperatures must be the same for dHvap calculation"
+
+    e_gas = pandas.read_csv(f"{gas_dir}/production.csv")["Potential Energy (kJ/mole)"].mean()
+    e_liquid = pandas.read_csv(f"{liquid_dir}/production.csv")["Potential Energy (kJ/mole)"].mean()
+
+    R = (1 * unit.avogadro_number * unit.boltzmann_constant).m_as("kJ/K")
+
+    dhvap = e_gas - e_liquid / n_molecules + R * gas_temperature  # kJ/mol
+
+    return {
+        "dhvap": dhvap,
+    }
diff --git a/tyff/compute/apps.py b/tyff/compute/apps.py
index 98cf8a2..8fff7ed 100644
--- a/tyff/compute/apps.py
+++ b/tyff/compute/apps.py
@@ -65,6 +65,26 @@ def run_production(
     return _run_production(production_config, equilibration_future, job_dir)
 
 
+@python_app
+def run_density_analysis(
+    job_dir: str,
+) -> dict[str, float]:
+    """Run a naive density analysis of production trajectories. For debugging only, not for tensor fitting."""
+    from tyff.compute._analyze import _run_density_analysis
+
+    return _run_density_analysis(job_dir)
+
+
+@python_app
+def run_dhvap_analysis(
+    job_dirs: list[str],
+) -> dict[str, float]:
+    """Run a naive DHVAP analysis of production trajectories. For debugging only, not for tensor fitting."""
+    from tyff.compute._analyze import _run_dhvap_analysis
+
+    return _run_dhvap_analysis(job_dirs)
+
+
 @python_app
 def create_jacobian(
     job_dir: str,

From d8dbf21b5d075800b4e5a57001833fd5e402bc7d Mon Sep 17 00:00:00 2001
From: "Matthew W. Thompson" <mattwthompson@protonmail.com>
Date: Mon, 28 Sep 2026 12:38:58 -0500
Subject: [PATCH 08/20] Dependency/tooling updates

---
 .pre-commit-config.yaml   |    2 +-
 pixi.lock                 | 6161 +++++++++++++++++--------------------
 pixi.toml                 |   13 +-
 tyff/compute/_jacobian.py |    2 +
 4 files changed, 2785 insertions(+), 3393 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 8af7e28..2dfa9bf 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -10,7 +10,7 @@ repos:
       exclude: '\.(json)$'
     - id: debug-statements
 - repo: https://github.com/astral-sh/ruff-pre-commit
-  rev: v0.16.7
+  rev: v0.16.9
   hooks:
     - id: ruff-check
       args: ["--fix"]
diff --git a/pixi.lock b/pixi.lock
index 92c03fe..cb2f9f2 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -21,7 +21,6 @@ environments:
       linux-64:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-8_kmp_llvm.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/aiohttp-3.14.3-py312h5d8c7f2_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/argon2-cffi-bindings-26.1.0-py312h5cc1888_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.10.1-h2d2dd48_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.9.13-h2c9d079_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.12.6-hb03c661_0.conda
@@ -49,15 +48,13 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.8-hebe6cf0_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/c-blosc2-3.2.3-hc31b594_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.4-h3394656_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-2.1.1-py312h703531f_3.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cftime-1.6.5-py312h0ec8046_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.3-py312h0a2e395_4.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvrtc-13.4.92-hd2095e1_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cytoolz-1.1.0-py312h5cc1888_3.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/debugpy-1.8.22-py312h80505a6_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/fmt-12.1.0-h76c4fd7_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.18.3-h4db4eae_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.65.0-py312hfd18d64_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.66.0-py312hfd18d64_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/freetype-2.14.3-ha770c72_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/frozenlist-1.8.0-py312h447239a_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/gflags-2.3.1-h54a6638_0.conda
@@ -65,7 +62,6 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/gmp-6.3.0-hfd2156b_3.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/gmpy2-2.3.1-py312h04c1168_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/greenlet-3.5.6-py312h80505a6_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/grpcio-1.78.1-py312h39ee1c6_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/hdf4-4.2.15-h2a13503_7.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/hdf5-2.1.0-nompi_hd4fcb43_104.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/hf-xet-1.5.2-py310hb823017_2.conda
@@ -96,14 +92,14 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20250104-pl5321h373387f_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-h280c20c_3.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-h5348a74_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.8.4-hd2095e1_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.8.5-hd2095e1_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.7.0-h81df57d_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libfreetype-2.14.3-ha770c72_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libfreetype6-2.14.3-h5e6c136_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-16.2.0-ha9f2e26_5.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-16.2.0-h69a702a_5.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-16.2.0-h69a702a_5.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-16.2.0-h6b99dfc_5.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-16.2.0-ha9f2e26_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-16.2.0-h69a702a_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-16.2.0-h69a702a_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-16.2.0-h6b99dfc_7.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libglib-2.90.0-h569388d_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-3.3.0-h25dbb67_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-3.3.0-hdbdcf42_1.conda
@@ -123,11 +119,10 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-6.33.5-h538a264_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libpython-3.12.14-h0c77377_3_cpython.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libre2-11-2025.11.05-h0dc7533_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libsodium-1.0.22-hebe6cf0_3.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.53.4-h0737f62_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.11.1-h6154650_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-16.2.0-h934c35e_5.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-16.2.0-hdf11a46_5.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-16.2.0-h934c35e_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-16.2.0-hdf11a46_7.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libthrift-0.22.0-h7d032f7_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.7.2-hcc2c06a_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libtorch-2.12.0-cpu_mkl_h55d9b97_100.conda
@@ -141,8 +136,8 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.15.1-h26afc86_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libzip-1.11.4-h6008cf6_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.2-h25fd6f3_3.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-23.1.1-h7148c6a_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/llvmlite-0.49.0-py312hb491018_4.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-23.1.2-h7148c6a_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/llvmlite-0.49.0-py312hb491018_5.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-4.4.5-py312hc6a72db_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.10.0-hee9eb32_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.3-py312h8a5da7c_1.conda
@@ -157,6 +152,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.6-hdb14827_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/netcdf4-1.7.4-nompi_py311hb115678_109.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/nlohmann_json-3.12.0-h54a6638_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/nnpops-0.7-cpu_py312h5b540b4_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/numba-0.67.0-py312h80505a6_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/numexpr-2.14.2-mkl_py312h67929eb_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-1.26.4-py312heda63a1_0.conda
@@ -171,13 +167,11 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/orc-2.3.0-h21090e2_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/packmol-21.2.1-h4537401_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pandas-2.1.4-py312hfb8ada1_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/pandoc-3.11-ha770c72_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.47-h8b3dc9c_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pillow-12.3.0-py312h38079b3_4.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pixman-0.46.4-h54a6638_3.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/prometheus-cpp-1.3.0-ha5d0236_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/propcache-0.5.2-py312h8a5da7c_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/protobuf-6.33.5-py312ha7b3241_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/psutil-7.2.2-py312h1b36aeb_3.conda
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@@ -190,20 +184,17 @@ environments:
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@@ -255,11 +233,9 @@ environments:
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+  - typing-extensions>=4.1 ; python_full_version < '3.11'
+  - rich>=13 ; extra == 'rich'
+  requires_python: '>=3.8'
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+  - pytest>=6.2.5
+  - pre-commit ; extra == 'dev'
+  - pytest-asyncio ; extra == 'dev'
+  - tox ; extra == 'dev'
+  requires_python: '>=3.10'
+- pypi: https://files.pythonhosted.org/packages/db/f3/3996583dd2906297a637af12114deddf7658af6e683fedb83be061983fb5/protobuf-7.36.2-cp310-abi3-manylinux2014_x86_64.whl
+  name: protobuf
+  version: 7.36.2
+  sha256: 89f23aa53c24553a2416fd4fd1ec06f74fa42b14b546d8883128813f775bbfd2
+  requires_python: '>=3.10'
+- pypi: https://files.pythonhosted.org/packages/dc/55/6467fde553886cb293e41538f3a8b4e4fd4688c6df242cf982162d8367fb/python_json_logger-4.2.0-py3-none-any.whl
+  name: python-json-logger
+  version: 4.2.0
+  sha256: 158a52126fcd6869e09574d2b66272666f3dc8f468c62637ef9a1fa883719cb9
+  requires_python: '>=3.10'
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   name: cryptography
   version: 50.0.1
@@ -21020,19 +20211,175 @@ packages:
   - typing-extensions>=4.13.2 ; python_full_version < '3.11'
   - bcrypt>=3.1.5 ; extra == 'ssh'
   requires_python: '!=3.9.0,>=3.9,!=3.9.1'
+- pypi: https://files.pythonhosted.org/packages/e2/a0/834886b30e3b885a5a96e8358f1b3917bc0bac43ae4178d77f0c210d079b/wcwidth-0.9.1-cp310-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
+  name: wcwidth
+  version: 0.9.1
+  sha256: 747fb724223f417a17541a95a17c1dac3a8ef9a0cf41684950f0eab191a35f65
+  requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/e2/cc/e097523dd85c9cf5d354f78310927f1656c422bd7b2613b2db3e3f9a0f2c/webcolors-25.10.0-py3-none-any.whl
+  name: webcolors
+  version: 25.10.0
+  sha256: 032c727334856fc0b968f63daa252a1ac93d33db2f5267756623c210e57a4f1d
+  requires_python: '>=3.10'
+- pypi: https://files.pythonhosted.org/packages/e4/e6/b4dedd1efea8a1e432123575896328dec83c26327164f7098db415d13f6f/vcs_versioning-2.5.0-py3-none-any.whl
+  name: vcs-versioning
+  version: 2.5.0
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+  - tomli>=1 ; python_full_version < '3.11'
+  - typing-extensions>=4.1 ; python_full_version < '3.11'
+  requires_python: '>=3.8'
+- pypi: https://files.pythonhosted.org/packages/e5/e2/c2e3abf398f80732e58b03be77bde9022550d221dd8781bf586bd4d97cc1/async_lru-2.3.0-py3-none-any.whl
+  name: async-lru
+  version: 2.3.0
+  sha256: eea27b01841909316f2cc739807acea1c623df2be8c5cfad7583286397bb8315
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+  requires_python: '>=3.10'
 - pypi: https://files.pythonhosted.org/packages/e6/c4/ebdf7837bc4ef6fd98cfb013c28855bb358467bf86c1af011bbc21e21df0/durationpy-0.11-py3-none-any.whl
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-  - pydantic>=2.11.0
-  - typing-extensions>=4.5.0
+  - types-pyyaml ; extra == 'dev'
+  - mypy ; extra == 'dev'
+  - flake8 ; extra == 'dev'
+  - flake8-annotations ; extra == 'dev'
+  - flake8-bandit ; extra == 'dev'
+  - flake8-bugbear ; extra == 'dev'
+  - flake8-commas ; extra == 'dev'
+  - flake8-comprehensions ; extra == 'dev'
+  - flake8-continuation ; extra == 'dev'
+  - flake8-datetimez ; extra == 'dev'
+  - flake8-docstrings ; extra == 'dev'
+  - flake8-import-order ; extra == 'dev'
+  - flake8-literal ; extra == 'dev'
+  - flake8-modern-annotations ; extra == 'dev'
+  - flake8-noqa ; extra == 'dev'
+  - flake8-pyproject ; extra == 'dev'
+  - flake8-requirements ; extra == 'dev'
+  - flake8-typechecking-import ; extra == 'dev'
+  - flake8-use-fstring ; extra == 'dev'
+  - pep8-naming ; extra == 'dev'
+  requires_python: '>=3.7'
+- pypi: https://files.pythonhosted.org/packages/e7/d2/0ae991f1b2181e5be49007c574710a800ad36c2978683addb3e67c474e55/argon2_cffi_bindings-26.1.0-cp310-abi3-macosx_11_0_arm64.whl
+  name: argon2-cffi-bindings
+  version: 26.1.0
+  sha256: 21ca0396fe5ec995dd54431c32698189666f9224810acfa752e50d2bd94d9df2
+  requires_dist:
+  - cffi>=1.0.1 ; python_full_version < '3.14'
+  - cffi>=2 ; python_full_version >= '3.14'
+  requires_python: '>=3.10'
+- pypi: https://files.pythonhosted.org/packages/e7/e7/80988e32bf6f73919a113473a604f5a8f09094de312b9d52b79c2df7612b/jupyter_core-5.9.1-py3-none-any.whl
+  name: jupyter-core
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+  sha256: ebf87fdc6073d142e114c72c9e29a9d7ca03fad818c5d300ce2adc1fb0743407
+  requires_dist:
+  - platformdirs>=2.5
+  - traitlets>=5.3
+  - intersphinx-registry ; extra == 'docs'
+  - myst-parser ; extra == 'docs'
+  - pydata-sphinx-theme ; extra == 'docs'
+  - sphinx-autodoc-typehints ; extra == 'docs'
+  - sphinxcontrib-spelling ; extra == 'docs'
+  - traitlets ; extra == 'docs'
+  - ipykernel ; extra == 'test'
+  - pre-commit ; extra == 'test'
+  - pytest-cov ; extra == 'test'
+  - pytest-timeout ; extra == 'test'
+  - pytest<9 ; extra == 'test'
   requires_python: '>=3.10'
+- pypi: https://files.pythonhosted.org/packages/e8/55/40e45bf052ee8ee12a4dfd785519660f8effa7b065442b91646ec6828619/pyjwt-2.15.0-py3-none-any.whl
+  name: pyjwt
+  version: 2.15.0
+  sha256: 7a3742debf6b879e912dbb9819ceec1594be812452b78c5f2e2dfc56564954f8
+  requires_dist:
+  - typing-extensions>=4.0 ; python_full_version < '3.11'
+  - cryptography>=3.4.0 ; extra == 'crypto'
+  requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/eb/6c/6fcde0c8f616ed360ffd3587f7db9e225a7e62b583a04494d2f069cf64ea/jupyter_events-0.12.1-py3-none-any.whl
+  name: jupyter-events
+  version: 0.12.1
+  sha256: c366585253f537a627da52fa7ca7410c5b5301fe893f511e7b077c2d93ec8bcf
+  requires_dist:
+  - jsonschema[format-nongpl]>=4.18.0
+  - packaging
+  - python-json-logger>=2.0.4
+  - pyyaml>=5.3
+  - referencing
+  - rfc3339-validator
+  - rfc3986-validator>=0.1.1
+  - traitlets>=5.3
+  - click ; extra == 'cli'
+  - rich ; extra == 'cli'
+  - jupyterlite-sphinx ; extra == 'docs'
+  - myst-parser ; extra == 'docs'
+  - pydata-sphinx-theme>=0.16 ; extra == 'docs'
+  - sphinx>=8 ; extra == 'docs'
+  - sphinxcontrib-spelling ; extra == 'docs'
+  - click ; extra == 'test'
+  - pre-commit ; extra == 'test'
+  - pytest-asyncio>=0.19.0 ; extra == 'test'
+  - pytest-console-scripts ; extra == 'test'
+  - pytest>=7.0 ; extra == 'test'
+  - rich ; extra == 'test'
+  requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/eb/a3/b69efbf4143b5b9859b977770bbbabcc2796b702fa69dc40271e45cd5a56/prometheus_client-0.26.0-py3-none-any.whl
+  name: prometheus-client
+  version: 0.26.0
+  sha256: fa93d06737aa02bacd05794768508bb97d2fbee28cb3bca04eaae92f0ca953d6
+  requires_dist:
+  - twisted ; extra == 'twisted'
+  - aiohttp ; extra == 'aiohttp'
+  - django ; extra == 'django'
+  requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/eb/be/59527c99478aade6bb33a68d72e6e18dd4e6ff6eacfc7d01bdb15bc76912/json5-0.15.0-py3-none-any.whl
+  name: json5
+  version: 0.15.0
+  sha256: 56636a30c0e8a4665fe2179c0212f32eae3796dea89ea6f649b9436ecdb39618
+  requires_python: '>=3.8.0'
+- pypi: https://files.pythonhosted.org/packages/ec/1e/32971905a7ab47f8b66866ed949fa48b104ba1c4a6fa57794c4f2c4b2cb8/markdown-3.11-py3-none-any.whl
+  name: markdown
+  version: '3.11'
+  sha256: cd6c89e7eb308c8b332ed673215a52d208a43f8bacc030b1419376129408719e
+  requires_dist:
+  - coverage ; extra == 'testing'
+  - pyyaml ; extra == 'testing'
+  - zensical==0.0.62 ; extra == 'docs'
+  - mdx-gh-links==0.4 ; extra == 'docs'
+  - mkdocstrings==1.0.6 ; extra == 'docs'
+  - mkdocstrings-python==1.16.8 ; extra == 'docs'
+  - pygments==2.21.0 ; extra == 'docs'
+  - pymdown-extensions==11.0.2 ; extra == 'docs'
+  - justhtml==3.11.2 ; extra == 'docs'
+  - ghp-import==2.1.0 ; extra == 'docs'
+  requires_python: '>=3.11'
+- pypi: https://files.pythonhosted.org/packages/ed/c9/d7977eaacb9df673210491da99e6a247e93df98c715fc43fd136ce1d3d33/arrow-1.4.0-py3-none-any.whl
+  name: arrow
+  version: 1.4.0
+  sha256: 749f0769958ebdc79c173ff0b0670d59051a535fa26e8eba02953dc19eb43205
+  requires_dist:
+  - python-dateutil>=2.7.0
+  - backports-zoneinfo==0.2.1 ; python_full_version < '3.9'
+  - tzdata ; python_full_version >= '3.9'
+  - doc8 ; extra == 'doc'
+  - sphinx>=7.0.0 ; extra == 'doc'
+  - sphinx-autobuild ; extra == 'doc'
+  - sphinx-autodoc-typehints ; extra == 'doc'
+  - sphinx-rtd-theme>=1.3.0 ; extra == 'doc'
+  - dateparser==1.* ; extra == 'test'
+  - pre-commit ; extra == 'test'
+  - pytest ; extra == 'test'
+  - pytest-cov ; extra == 'test'
+  - pytest-mock ; extra == 'test'
+  - pytz==2025.2 ; extra == 'test'
+  - simplejson==3.* ; extra == 'test'
+  requires_python: '>=3.8'
 - pypi: https://files.pythonhosted.org/packages/ed/e5/d1b15eeea292f5b58a80b83fcc746aae42b91ade29f5f7fbeb442811e2bb/panel_material_ui-0.15.0-py3-none-any.whl
   name: panel-material-ui
   version: 0.15.0
@@ -21051,6 +20398,11 @@ packages:
   - types-requests ; extra == 'mypy'
   - typing-extensions ; extra == 'mypy'
   requires_python: '>=3.10'
+- pypi: https://files.pythonhosted.org/packages/ef/af/4fbc8cab944db5d21b7e2a5b8e9211a03a79852b1157e2c102fcc61ac440/pandocfilters-1.5.1-py2.py3-none-any.whl
+  name: pandocfilters
+  version: 1.5.1
+  sha256: 93be382804a9cdb0a7267585f157e5d1731bbe5545a85b268d6f5fe6232de2bc
+  requires_python: '>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*'
 - pypi: https://files.pythonhosted.org/packages/f0/b4/50940504466689cda65680c9e9a1e518e5750c10490639fa687489ac7013/setproctitle-1.3.7-cp312-cp312-macosx_11_0_arm64.whl
   name: setproctitle
   version: 1.3.7
@@ -21058,6 +20410,19 @@ packages:
   requires_dist:
   - pytest ; extra == 'test'
   requires_python: '>=3.8'
+- pypi: https://files.pythonhosted.org/packages/f1/7b/ce1eafaf1a76852e2ec9b22edecf1daa58175c090266e9f6c64afcd81d91/stack_data-0.6.3-py3-none-any.whl
+  name: stack-data
+  version: 0.6.3
+  sha256: d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695
+  requires_dist:
+  - executing>=1.2.0
+  - asttokens>=2.1.0
+  - pure-eval
+  - pytest ; extra == 'tests'
+  - typeguard ; extra == 'tests'
+  - pygments ; extra == 'tests'
+  - littleutils ; extra == 'tests'
+  - cython ; extra == 'tests'
 - pypi: https://files.pythonhosted.org/packages/f1/d9/7fb5aa316bc299258e68c73ba3bddbc499654a07f151cba08f6153988714/pathspec-1.1.1-py3-none-any.whl
   name: pathspec
   version: 1.1.1
@@ -21067,6 +20432,33 @@ packages:
   - typing-extensions>=4 ; extra == 'optional'
   - google-re2>=1.1 ; extra == 're2'
   requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/f7/b3/988928029b0e68de7ac587c2e1ccd379aa18e7c0aa7cc426628145a032fe/sqlmodel-0.0.47-py3-none-any.whl
+  name: sqlmodel
+  version: 0.0.47
+  sha256: 165294386c3e4b25fa02f151ca1abb0cbf5fdb76f6093a38aca33fdc30dfd485
+  requires_dist:
+  - sqlalchemy>=2.0.14,<2.1.0
+  - pydantic>=2.11.0
+  - typing-extensions>=4.5.0
+  requires_python: '>=3.10'
+- pypi: https://files.pythonhosted.org/packages/f9/33/bd5b9137445ea4b680023eb0469b2bb969d61303dedb2aac6560ff3d14a1/notebook_shim-0.2.4-py3-none-any.whl
+  name: notebook-shim
+  version: 0.2.4
+  sha256: 411a5be4e9dc882a074ccbcae671eda64cceb068767e9a3419096986560e1cef
+  requires_dist:
+  - jupyter-server>=1.8,<3
+  - pytest ; extra == 'test'
+  - pytest-console-scripts ; extra == 'test'
+  - pytest-jupyter ; extra == 'test'
+  - pytest-tornasync ; extra == 'test'
+  requires_python: '>=3.7'
+- pypi: https://files.pythonhosted.org/packages/fa/70/fffc9613501877c0a10a1ef73a165e6ded3e53f6e7502227fef56d973933/identify-2.6.20-py2.py3-none-any.whl
+  name: identify
+  version: 2.6.20
+  sha256: 6a16b69b93187244e0548cbfd25b3e4a6f9a7a2ad784625c3bec2b8d27b81aaa
+  requires_dist:
+  - ukkonen ; extra == 'license'
+  requires_python: '>=3.10'
 - pypi: https://files.pythonhosted.org/packages/fd/de/af09ae53d0e8dd58b304291af177076b9dda6ce6fecd7a41674acf8f6638/parsl-2024.4.8-py3-none-any.whl
   name: parsl
   version: 2024.4.8
@@ -21135,8 +20527,3 @@ packages:
   - python-daemon ; extra == 'visualization'
   - work-queue ; extra == 'workqueue'
   requires_python: '>=3.8.0'
-- pypi: https://files.pythonhosted.org/packages/fe/1f/e6a712e3b47cad4a40600134105ed193cb773f618a42eb7ba323cb812cc0/ruff-0.16.8-py3-none-macosx_11_0_arm64.whl
-  name: ruff
-  version: 0.16.8
-  sha256: 7ea781c7f2afba8c6a505ea0fb3f994020249e0c450635f5381286fea6b46170
-  requires_python: '>=3.7'
diff --git a/pixi.toml b/pixi.toml
index d9c72ac..d29022e 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -33,6 +33,12 @@ rdkit-stubs = "*"
 parsl = "*"
 rich = "*"
 tyff = { path = ".", editable = true }
+# probably optional-ish below here
+jupyter = "*"
+nbconvert = "*"
+tensorboard = "*"
+tensorboardx = "*"
+uncertainties = "*"
 
 [dependencies]
 openmm = "8.4.*" # need to help Pixi, since conda solve constraints PyPI solve
@@ -42,15 +48,12 @@ openff-nagl-base = "*"
 openff-nagl-models = "*"
 openmmtools = "*"
 pydantic-units = "*"
+sqlalchemy = ">=2.0.14,<2.1.0"
 pytorch = "*"
+NNPOps = "*"
 datasets = "*"
 bokeh = "3.9.*"
 # probably optional-ish below here
-jupyter = "*"
-nbconvert = "*"
-tensorboard = "*"
-tensorboardx = "*"
-uncertainties = "*"
 openff-packmol = "0.1.*"
 openff-evaluator-base = "*"
 # shims
diff --git a/tyff/compute/_jacobian.py b/tyff/compute/_jacobian.py
index cff228d..2d0bcb1 100644
--- a/tyff/compute/_jacobian.py
+++ b/tyff/compute/_jacobian.py
@@ -43,6 +43,8 @@ def _get_ensemble_average_and_jacobian(
         with open(interchange_path) as f:
             interchanges.append(Interchange.model_validate_json(f.read()))
 
+    assert len(interchanges) > 0, "Did not find single-molecule `Interchange`s as expected"
+
     tensor_force_field, tensor_topologies = tyff.converters.convert_interchange(interchanges)
 
     # must sync this up with tyff/compute/_pack.py if ever either change

From 4b5c2337fed9e5c9f5f32426ad8908638086b3d8 Mon Sep 17 00:00:00 2001
From: "Matthew W. Thompson" <mattwthompson@protonmail.com>
Date: Mon, 28 Sep 2026 12:50:09 -0500
Subject: [PATCH 09/20] Also serialize ensemble averages and Jacobian matrices

---
 tyff/compute/_jacobian.py | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/tyff/compute/_jacobian.py b/tyff/compute/_jacobian.py
index 2d0bcb1..60a747f 100644
--- a/tyff/compute/_jacobian.py
+++ b/tyff/compute/_jacobian.py
@@ -90,4 +90,12 @@ def _get_ensemble_average_and_jacobian(
             for name in sorted(means)  # Claude doesn't think this should be sorted
         ]
     )
+
+    # save ensemble averages and jacobian in each job dir, consider revisiting this decision in the future
+    with open(f"{job_dir}/ensemble_averages.json", "w") as f:
+        json.dump({name: value.detach().tolist() for name, value in means.items()}, f)
+
+    with open(f"{job_dir}/jacobian.pt", "wb") as f:
+        torch.save(jacobian.detach(), f)
+
     return {name: value.detach() for name, value in means.items()}, jacobian.detach()

From 009044c89c5a3e7e4b602216928a5716aa4a96c9 Mon Sep 17 00:00:00 2001
From: "Matthew W. Thompson" <mattwthompson@protonmail.com>
Date: Mon, 28 Sep 2026 14:22:56 -0500
Subject: [PATCH 10/20] Update example, clean up apps/workflow manager

---
 examples/density/run.py   | 28 ++++++++++++++++++++++++++++
 tyff/compute/_jacobian.py |  6 ++++++
 tyff/compute/apps.py      |  6 ++++--
 tyff/compute/workflow.py  | 14 +++++++++-----
 4 files changed, 47 insertions(+), 7 deletions(-)

diff --git a/examples/density/run.py b/examples/density/run.py
index 22e79be..60d5443 100644
--- a/examples/density/run.py
+++ b/examples/density/run.py
@@ -1,3 +1,4 @@
+import json
 import pathlib
 import socket
 
@@ -7,6 +8,8 @@
 
 from tyff.compute.configs import hpc3_config, local_config
 from tyff.compute.fetch import fetch_trajectory_paths_from_target
+from tyff.compute.jobs import make_job_id
+from tyff.compute.prep import _compute_configs_from_data_entry
 from tyff.compute.workflow import SimulationWorkflow
 from tyff.targets.thermo import DataEntry
 
@@ -73,6 +76,7 @@
             n_replicates=5,
         )
 
+        # naive property estimate, no tensor math
         for density_target in density_targets:
             workflow.estimate_target(
                 density_target,
@@ -94,6 +98,7 @@
             n_replicates=3,
         )
 
+        # naive property estimate, no tensor math
         for density_target in density_targets:
             workflow.estimate_target(
                 density_target,
@@ -104,6 +109,29 @@
 
 # TODO: Show how to check status while running
 
+# this generally happens under the hood
+compute_configs = [
+    _compute_configs_from_data_entry(
+        target,
+        force_field="openff-2.3.0.offxml",
+        n_molecules=200,
+    )
+    for target in density_targets
+]
+
+print(f"Number of targets for which compute configs were generated: {len(compute_configs)}")
+print(f"Number of replicates for each target: {[len(replicates) for replicates in compute_configs]}")
+
+# this is a list of job directories of each individual job (total of 9, 3x replicates of 3 targets)
+job_ids = [make_job_id(liquid_job) for replicate in compute_configs for row in replicate for liquid_job in row]
+
+for job_id in job_ids:
+    print(job_id)
+    print(json.loads(open(f"density_example/{job_id}/compute_config.json").read()))
+    print(json.loads(open(f"density_example/{job_id}/ensemble_averages.json").read()))
+
+# now for the old-school statistics, etc.
+
 # get trajectory paths from root job directory and per-target info (without knowing internal compute configs)
 trajectory_paths = [
     fetch_trajectory_paths_from_target(
diff --git a/tyff/compute/_jacobian.py b/tyff/compute/_jacobian.py
index 60a747f..d445f71 100644
--- a/tyff/compute/_jacobian.py
+++ b/tyff/compute/_jacobian.py
@@ -7,10 +7,12 @@
 import torch
 from openff.interchange import Interchange
 
+from tyff.compute._files import ProductionFiles
 from tyff.configs.liquid import BulkLiquid
 
 
 def _get_ensemble_average_and_jacobian(
+    production_future: dict[str, ProductionFiles],
     job_dir: str,
 ) -> tuple[dict[str, torch.Tensor], torch.Tensor]:
     import openmm.unit
@@ -18,6 +20,10 @@ def _get_ensemble_average_and_jacobian(
 
     import tyff.mm
     from tyff.mm._ops import _pack_force_field, _unpack_force_field
+
+    # try to safeguard against the previous step failing
+    assert pathlib.Path(production_future["simulation_files"]["msgpack_trajectory"].filepath).exists()
+
     # the arguments we really care about are:
     #     system: tyff.TensorSystem,
     #     frames_path: pathlib.Path,
diff --git a/tyff/compute/apps.py b/tyff/compute/apps.py
index 8fff7ed..d0f349d 100644
--- a/tyff/compute/apps.py
+++ b/tyff/compute/apps.py
@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import torch
 from parsl import python_app
 
 from tyff.compute._equilibrate import EquilibrationConfig
@@ -87,8 +88,9 @@ def run_dhvap_analysis(
 
 @python_app
 def create_jacobian(
+    production_future: dict[str, ProductionFiles],
     job_dir: str,
-) -> dict[str, float]:
+) -> tuple[dict[str, torch.Tensor], torch.Tensor]:
     from tyff.compute._jacobian import _get_ensemble_average_and_jacobian
 
-    return _get_ensemble_average_and_jacobian(job_dir)
+    return _get_ensemble_average_and_jacobian(production_future, job_dir)
diff --git a/tyff/compute/workflow.py b/tyff/compute/workflow.py
index 88bef12..442d67f 100644
--- a/tyff/compute/workflow.py
+++ b/tyff/compute/workflow.py
@@ -5,6 +5,7 @@
 
 import numpy
 import parsl
+import torch
 from parsl import File
 from rich import print
 
@@ -12,6 +13,7 @@
     ProductionFiles,
 )
 from tyff.compute.apps import (
+    create_jacobian,
     minimize_energy,
     prepare_openmm_system,
     prepare_packed_topology,
@@ -194,12 +196,12 @@ def run(self, compute_configs: Sequence[BaseComputeConfig]):
 
         return results
 
-    def _run_liquid_workflow(self, compute_config: BaseComputeConfig) -> dict[str, str | dict[str, ProductionFiles]]:
+    def _run_liquid_workflow(self, compute_config: BaseComputeConfig) -> tuple[dict[str, torch.Tensor], torch.Tensor]:
         return self._common_run(
             compute_config=compute_config,
         )
 
-    def _run_gas_workflow(self, compute_config: BaseComputeConfig) -> dict[str, str | dict[str, ProductionFiles]]:
+    def _run_gas_workflow(self, compute_config: BaseComputeConfig) -> tuple[dict[str, torch.Tensor], torch.Tensor]:
         assert compute_config["n_molecules"] == 1, (
             f"Gas workflow only supports single-molecule simulations, but got {compute_config['n_molecules']=}"
             f"and, more generally, {compute_config=}"
@@ -209,7 +211,7 @@ def _run_gas_workflow(self, compute_config: BaseComputeConfig) -> dict[str, str
             compute_config=compute_config,
         )
 
-    def _common_run(self, compute_config: BaseComputeConfig) -> dict[str, str | dict[str, ProductionFiles]]:
+    def _common_run(self, compute_config: BaseComputeConfig) -> tuple[dict[str, torch.Tensor], torch.Tensor]:
         """Common code in _run_liquid_workflow and _run_gas_workflow."""
         job_id = make_job_id(compute_config)
         job_dir = get_job_paths(self.base_dir, job_id)["root"]
@@ -225,7 +227,7 @@ def _common_run(self, compute_config: BaseComputeConfig) -> dict[str, str | dict
                 indent=4,
             )
 
-        if pathlib.Path(job_dir, "production_trajectory.dcd").exists():
+        if pathlib.Path(job_dir, "ensemble_averages.json").exists():
             logger.info(f"short-circuiting {job_id}!")
             # already done, skip
 
@@ -263,7 +265,9 @@ def _common_run(self, compute_config: BaseComputeConfig) -> dict[str, str | dict
             job_dir=job_dir,
         )
 
-        return {"job_id": job_id, "future": production_future}
+        jacobian_future = create_jacobian(production_future=production_future, job_dir=job_dir)
+
+        return {"job_id": job_id, "future": jacobian_future}
 
     def shutdown(self):
         parsl.clear()

From 8fe27a30700e8f3159ff5d42b10b949c33ecdd7f Mon Sep 17 00:00:00 2001
From: "Matthew W. Thompson" <mattwthompson@protonmail.com>
Date: Mon, 28 Sep 2026 17:28:26 -0500
Subject: [PATCH 11/20] Do not sort

---
 tyff/compute/_jacobian.py | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/tyff/compute/_jacobian.py b/tyff/compute/_jacobian.py
index d445f71..f883b2d 100644
--- a/tyff/compute/_jacobian.py
+++ b/tyff/compute/_jacobian.py
@@ -89,13 +89,13 @@ def _get_ensemble_average_and_jacobian(
         None if pressure is None else pressure * openmm.unit.atmosphere,
     )
 
-    # Jacobian rows follow sorted observable names; columns follow packed parameters.
-    jacobian = torch.stack(
-        [
-            torch.autograd.grad(means[name], parameters, retain_graph=True)[0]
-            for name in sorted(means)  # Claude doesn't think this should be sorted
-        ]
-    )
+    # originally this was sorted(means),
+    # https://github.com/openforcefield/tyff/pull/173#discussion_r4126720329
+    #
+    # if that's changed back then we need to make BOTH
+    # the jacobian and ensemble averages sorted in serialization AND return values
+    # https://github.com/openforcefield/tyff/pull/173#discussion_r4126720329
+    jacobian = torch.stack([torch.autograd.grad(means[name], parameters, retain_graph=True)[0] for name in means])
 
     # save ensemble averages and jacobian in each job dir, consider revisiting this decision in the future
     with open(f"{job_dir}/ensemble_averages.json", "w") as f:

From e10511180640eb787c99618868658d0e6fefb346 Mon Sep 17 00:00:00 2001
From: "Matthew W. Thompson" <mattwthompson@protonmail.com>
Date: Tue, 29 Sep 2026 15:05:02 -0500
Subject: [PATCH 12/20] Serialize and de-serialize `TensorForceField`

---
 tyff/_serialization.py            | 115 ++++++++++++++++++++++++++++++
 tyff/_tests/test_serialization.py |  69 ++++++++++++++++++
 tyff/compute/_jacobian.py         |   2 +-
 3 files changed, 185 insertions(+), 1 deletion(-)
 create mode 100644 tyff/_serialization.py
 create mode 100644 tyff/_tests/test_serialization.py

diff --git a/tyff/_serialization.py b/tyff/_serialization.py
new file mode 100644
index 0000000..f7914c3
--- /dev/null
+++ b/tyff/_serialization.py
@@ -0,0 +1,115 @@
+from typing import Annotated, Any
+
+import openff.interchange
+import openff.units
+import torch
+from pydantic import BeforeValidator, PlainSerializer
+
+from tyff._models import TensorForceField, TensorPotential, TensorVSites
+
+
+def _dump_tensor(t: torch.Tensor) -> dict[str, Any]:
+    return {"dtype": str(t.dtype).removeprefix("torch."), "shape": list(t.shape), "data": t.flatten().tolist()}
+
+
+def _load_tensor(v: Any) -> torch.Tensor:
+    if isinstance(v, torch.Tensor):
+        return v
+    return torch.tensor(v["data"], dtype=getattr(torch, v["dtype"])).reshape(v["shape"])
+
+
+_Tensor = Annotated[torch.Tensor, BeforeValidator(_load_tensor), PlainSerializer(_dump_tensor)]
+
+
+def _dump_sparse(t: torch.Tensor) -> dict[str, Any]:
+    t = t.coalesce()
+    return {
+        "dtype": str(t.dtype).removeprefix("torch."),
+        "shape": list(t.shape),
+        "indices": t.indices().tolist(),
+        "values": t.values().tolist(),
+    }
+
+
+def _load_sparse(v: Any) -> torch.Tensor:
+    if isinstance(v, torch.Tensor):
+        return v
+    return torch.sparse_coo_tensor(
+        v["indices"], v["values"], size=v["shape"], dtype=getattr(torch, v["dtype"])
+    ).coalesce()
+
+
+def _dump_exceptions(d: dict[tuple[int, int], int] | None) -> list[tuple[int, int, int]] | None:
+    return None if d is None else [(i, j, v) for (i, j), v in d.items()]
+
+
+def _load_exceptions(v: Any) -> dict[tuple[int, int], int] | None:
+    if v is None or isinstance(v, dict):
+        return v
+    return {(i, j): val for i, j, val in v}
+
+
+# tyff/_serialization.py
+def dump_tensor_potential(p: TensorPotential) -> dict:
+    return {
+        "type": p.type,
+        "fn": p.fn,
+        "parameters": _dump_tensor(p.parameters),
+        "parameter_keys": [k.model_dump() for k in p.parameter_keys],
+        "parameter_cols": list(p.parameter_cols),
+        "parameter_units": [str(u) for u in p.parameter_units],
+        "attributes": None if p.attributes is None else _dump_tensor(p.attributes),
+        "attribute_cols": p.attribute_cols,
+        "attribute_units": None if p.attribute_units is None else [str(u) for u in p.attribute_units],
+        "exceptions": _dump_exceptions(p.exceptions),
+    }
+
+
+def load_tensor_potential(d: dict) -> TensorPotential:
+    return TensorPotential(
+        type=d["type"],
+        fn=d["fn"],
+        parameters=_load_tensor(d["parameters"]),
+        parameter_keys=[openff.interchange.models.PotentialKey.model_validate(k) for k in d["parameter_keys"]],
+        parameter_cols=tuple(d["parameter_cols"]),
+        parameter_units=tuple(openff.units.unit.Unit(u) for u in d["parameter_units"]),
+        attributes=None if d["attributes"] is None else _load_tensor(d["attributes"]),
+        attribute_cols=tuple(d["attribute_cols"]) if d["attribute_cols"] is not None else None,
+        attribute_units=(
+            None if d["attribute_units"] is None else tuple(openff.units.unit.Unit(u) for u in d["attribute_units"])
+        ),
+        exceptions=_load_exceptions(d["exceptions"]),
+    )
+
+
+# tyff/_serialization.py (continued)
+
+
+def dump_tensor_vsites(v: TensorVSites) -> dict:
+    return {
+        "keys": [k.model_dump() for k in v.keys],
+        "weights": [_dump_tensor(w) for w in v.weights],
+        "parameters": _dump_tensor(v.parameters),
+    }
+
+
+def load_tensor_vsites(d: dict) -> TensorVSites:
+    return TensorVSites(
+        keys=[openff.interchange.models.VirtualSiteKey.model_validate(k) for k in d["keys"]],
+        weights=[_load_tensor(w) for w in d["weights"]],
+        parameters=_load_tensor(d["parameters"]),
+    )
+
+
+def dump_tensor_force_field(tff: TensorForceField) -> dict:
+    return {
+        "potentials": [dump_tensor_potential(p) for p in tff.potentials],
+        "v_sites": None if tff.v_sites is None else dump_tensor_vsites(tff.v_sites),
+    }
+
+
+def load_tensor_force_field(d: dict) -> TensorForceField:
+    return TensorForceField(
+        potentials=[load_tensor_potential(p) for p in d["potentials"]],
+        v_sites=None if d["v_sites"] is None else load_tensor_vsites(d["v_sites"]),
+    )
diff --git a/tyff/_tests/test_serialization.py b/tyff/_tests/test_serialization.py
new file mode 100644
index 0000000..6f0340a
--- /dev/null
+++ b/tyff/_tests/test_serialization.py
@@ -0,0 +1,69 @@
+import torch
+import json
+
+from tyff._serialization import dump_tensor_force_field, load_tensor_force_field
+from tyff._tests.utils import system_from_smiles
+from tyff._models import TensorForceField
+
+def compare_tensor_force_fields(
+        force_field1:  TensorForceField,
+        force_field2: TensorForceField,
+) -> bool:
+    if len(force_field1.potentials) != len(force_field2.potentials):
+        print("Failed: Number of potentials do not match")
+        return False
+
+    for potential1, potential2 in zip(force_field1.potentials, force_field2.potentials):
+        if potential1.type != potential2.type:
+            print(f"Failed: potential type mismatch ({potential1.type} != {potential2.type})")
+            return False
+        if potential1.fn != potential2.fn:
+            print(f"Failed: potential fn mismatch ({potential1.fn} != {potential2.fn})")
+            return False
+        if potential1.parameter_units != potential2.parameter_units:
+            print(f"Failed: potential parameter_units mismatch ({potential1.parameter_units} != {potential2.parameter_units})")
+            return False
+        if potential1.parameter_keys != potential2.parameter_keys:
+            print(f"Failed: potential parameter_keys mismatch ({potential1.parameter_keys} != {potential2.parameter_keys})")
+            return False
+        if potential1.parameter_cols != potential2.parameter_cols:
+            print(f"Failed: potential parameter_cols mismatch ({potential1.parameter_cols} != {potential2.parameter_cols})")
+            return False
+        if not torch.equal(potential1.parameters, potential2.parameters):
+            print("Failed: potential parameters are not equal")
+            return False
+
+        # potential.attributes can be None
+        if potential1.attributes is None:
+            if potential2.attributes is not None:
+                print("Failed: potential1.attributes is None but potential2.attributes is not None")
+                return False
+        else:
+            if not torch.equal(potential1.attributes, potential2.attributes):
+                print("Failed: potential1.attributes and potential2.attributes are not equal")
+                return False
+
+        if potential1.attribute_units != potential2.attribute_units:
+            print(f"Failed: potential attribute_units mismatch ({potential1.attribute_units} != {potential2.attribute_units})")
+            return False
+        if potential1.attribute_cols != potential2.attribute_cols:
+            print(f"Failed: potential attribute_cols mismatch ({potential1.attribute_cols} != {potential2.attribute_cols})")
+            return False
+
+    return True
+
+def test_basic_serialization(default_force_field, tmp_path):
+
+    _, tensor_force_field = system_from_smiles(
+        ["O"],
+        [1],
+        default_force_field,
+    )
+
+    with open(tmp_path / "tensor_force_field.json", "w") as f:
+        json.dump(dump_tensor_force_field(tensor_force_field), f)
+
+    with open(tmp_path / "tensor_force_field.json") as f:
+        loaded = load_tensor_force_field(json.load(f))
+
+    assert compare_tensor_force_fields(tensor_force_field, loaded)
diff --git a/tyff/compute/_jacobian.py b/tyff/compute/_jacobian.py
index f883b2d..2181f35 100644
--- a/tyff/compute/_jacobian.py
+++ b/tyff/compute/_jacobian.py
@@ -44,7 +44,7 @@ def _get_ensemble_average_and_jacobian(
         unique_molecule_index = int(pathlib.Path(interchange_path).stem.split("single_molecule_interchange_")[-1])
 
         # hope we're loading up the single-molecule interchanges in the same order as we have unique molecules
-        assert unique_molecule_index == path_index
+        assert unique_molecule_index == path_index, (unique_molecule_index, path_index)
 
         with open(interchange_path) as f:
             interchanges.append(Interchange.model_validate_json(f.read()))

From c52e67f054219e4eee7cb48ddeeb784ed2e4c3e1 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Tue, 29 Sep 2026 20:05:18 +0000
Subject: [PATCH 13/20] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 tyff/_tests/test_serialization.py | 31 ++++++++++++++++++++++---------
 1 file changed, 22 insertions(+), 9 deletions(-)

diff --git a/tyff/_tests/test_serialization.py b/tyff/_tests/test_serialization.py
index 6f0340a..9808f90 100644
--- a/tyff/_tests/test_serialization.py
+++ b/tyff/_tests/test_serialization.py
@@ -1,13 +1,15 @@
-import torch
 import json
 
+import torch
+
+from tyff._models import TensorForceField
 from tyff._serialization import dump_tensor_force_field, load_tensor_force_field
 from tyff._tests.utils import system_from_smiles
-from tyff._models import TensorForceField
+
 
 def compare_tensor_force_fields(
-        force_field1:  TensorForceField,
-        force_field2: TensorForceField,
+    force_field1: TensorForceField,
+    force_field2: TensorForceField,
 ) -> bool:
     if len(force_field1.potentials) != len(force_field2.potentials):
         print("Failed: Number of potentials do not match")
@@ -21,13 +23,19 @@ def compare_tensor_force_fields(
             print(f"Failed: potential fn mismatch ({potential1.fn} != {potential2.fn})")
             return False
         if potential1.parameter_units != potential2.parameter_units:
-            print(f"Failed: potential parameter_units mismatch ({potential1.parameter_units} != {potential2.parameter_units})")
+            print(
+                f"Failed: potential parameter_units mismatch ({potential1.parameter_units} != {potential2.parameter_units})"
+            )
             return False
         if potential1.parameter_keys != potential2.parameter_keys:
-            print(f"Failed: potential parameter_keys mismatch ({potential1.parameter_keys} != {potential2.parameter_keys})")
+            print(
+                f"Failed: potential parameter_keys mismatch ({potential1.parameter_keys} != {potential2.parameter_keys})"
+            )
             return False
         if potential1.parameter_cols != potential2.parameter_cols:
-            print(f"Failed: potential parameter_cols mismatch ({potential1.parameter_cols} != {potential2.parameter_cols})")
+            print(
+                f"Failed: potential parameter_cols mismatch ({potential1.parameter_cols} != {potential2.parameter_cols})"
+            )
             return False
         if not torch.equal(potential1.parameters, potential2.parameters):
             print("Failed: potential parameters are not equal")
@@ -44,14 +52,19 @@ def compare_tensor_force_fields(
                 return False
 
         if potential1.attribute_units != potential2.attribute_units:
-            print(f"Failed: potential attribute_units mismatch ({potential1.attribute_units} != {potential2.attribute_units})")
+            print(
+                f"Failed: potential attribute_units mismatch ({potential1.attribute_units} != {potential2.attribute_units})"
+            )
             return False
         if potential1.attribute_cols != potential2.attribute_cols:
-            print(f"Failed: potential attribute_cols mismatch ({potential1.attribute_cols} != {potential2.attribute_cols})")
+            print(
+                f"Failed: potential attribute_cols mismatch ({potential1.attribute_cols} != {potential2.attribute_cols})"
+            )
             return False
 
     return True
 
+
 def test_basic_serialization(default_force_field, tmp_path):
 
     _, tensor_force_field = system_from_smiles(

From 9fe39f1c59d757df24d780e037cc1ae9677dadda Mon Sep 17 00:00:00 2001
From: "Matthew W. Thompson" <mattwthompson@protonmail.com>
Date: Wed, 30 Sep 2026 11:30:59 -0500
Subject: [PATCH 14/20] Debug/fixes

---
 tyff/_tests/test_serialization.py | 22 +++++++---------------
 tyff/compute/_jacobian.py         |  5 +++--
 tyff/compute/workflow.py          | 18 ++++++++++++++++++
 3 files changed, 28 insertions(+), 17 deletions(-)

diff --git a/tyff/_tests/test_serialization.py b/tyff/_tests/test_serialization.py
index 6f0340a..3ca64bc 100644
--- a/tyff/_tests/test_serialization.py
+++ b/tyff/_tests/test_serialization.py
@@ -1,57 +1,49 @@
-import torch
 import json
 
+import torch
+
+from tyff._models import TensorForceField
 from tyff._serialization import dump_tensor_force_field, load_tensor_force_field
 from tyff._tests.utils import system_from_smiles
-from tyff._models import TensorForceField
+
 
 def compare_tensor_force_fields(
-        force_field1:  TensorForceField,
-        force_field2: TensorForceField,
+    force_field1: TensorForceField,
+    force_field2: TensorForceField,
 ) -> bool:
     if len(force_field1.potentials) != len(force_field2.potentials):
-        print("Failed: Number of potentials do not match")
         return False
 
     for potential1, potential2 in zip(force_field1.potentials, force_field2.potentials):
         if potential1.type != potential2.type:
-            print(f"Failed: potential type mismatch ({potential1.type} != {potential2.type})")
             return False
         if potential1.fn != potential2.fn:
-            print(f"Failed: potential fn mismatch ({potential1.fn} != {potential2.fn})")
             return False
         if potential1.parameter_units != potential2.parameter_units:
-            print(f"Failed: potential parameter_units mismatch ({potential1.parameter_units} != {potential2.parameter_units})")
             return False
         if potential1.parameter_keys != potential2.parameter_keys:
-            print(f"Failed: potential parameter_keys mismatch ({potential1.parameter_keys} != {potential2.parameter_keys})")
             return False
         if potential1.parameter_cols != potential2.parameter_cols:
-            print(f"Failed: potential parameter_cols mismatch ({potential1.parameter_cols} != {potential2.parameter_cols})")
             return False
         if not torch.equal(potential1.parameters, potential2.parameters):
-            print("Failed: potential parameters are not equal")
             return False
 
         # potential.attributes can be None
         if potential1.attributes is None:
             if potential2.attributes is not None:
-                print("Failed: potential1.attributes is None but potential2.attributes is not None")
                 return False
         else:
             if not torch.equal(potential1.attributes, potential2.attributes):
-                print("Failed: potential1.attributes and potential2.attributes are not equal")
                 return False
 
         if potential1.attribute_units != potential2.attribute_units:
-            print(f"Failed: potential attribute_units mismatch ({potential1.attribute_units} != {potential2.attribute_units})")
             return False
         if potential1.attribute_cols != potential2.attribute_cols:
-            print(f"Failed: potential attribute_cols mismatch ({potential1.attribute_cols} != {potential2.attribute_cols})")
             return False
 
     return True
 
+
 def test_basic_serialization(default_force_field, tmp_path):
 
     _, tensor_force_field = system_from_smiles(
diff --git a/tyff/compute/_jacobian.py b/tyff/compute/_jacobian.py
index 2181f35..31fe360 100644
--- a/tyff/compute/_jacobian.py
+++ b/tyff/compute/_jacobian.py
@@ -13,6 +13,7 @@
 
 def _get_ensemble_average_and_jacobian(
     production_future: dict[str, ProductionFiles],
+    interchanges_path: str | pathlib.Path,
     job_dir: str,
 ) -> tuple[dict[str, torch.Tensor], torch.Tensor]:
     import openmm.unit
@@ -40,8 +41,8 @@ def _get_ensemble_average_and_jacobian(
     pressure = compute_config.get("pressure")  # atmosphere, float | None
 
     interchanges = []
-    for path_index, interchange_path in enumerate(glob.glob(f"{job_dir}/single_molecule_interchange_*.json")):
-        unique_molecule_index = int(pathlib.Path(interchange_path).stem.split("single_molecule_interchange_")[-1])
+    for path_index, interchange_path in enumerate(sorted(glob.glob(f"{interchanges_path}/interchange_*.json"))):
+        unique_molecule_index = int(pathlib.Path(interchange_path).stem.split("interchange_")[-1])
 
         # hope we're loading up the single-molecule interchanges in the same order as we have unique molecules
         assert unique_molecule_index == path_index, (unique_molecule_index, path_index)
diff --git a/tyff/compute/workflow.py b/tyff/compute/workflow.py
index 442d67f..04a990a 100644
--- a/tyff/compute/workflow.py
+++ b/tyff/compute/workflow.py
@@ -120,6 +120,24 @@ def submit_target_batch(
         n_molecules: int,
         n_replicates: int = 3,
     ):
+        import openff.toolkit
+
+        unique_smiles = set()
+
+        for target in target_configs:
+            for smiles_ in target["smiles"]:
+                unique_smiles.add(smiles_)
+
+        (pathlib.Path(self.base_dir) / "interchanges").mkdir(exist_ok=True)
+
+        force_field_ = openff.toolkit.ForceField(force_field)
+        for index, unique_smiles_ in enumerate(unique_smiles):
+            interchange = force_field_.create_interchange(
+                openff.toolkit.Molecule.from_smiles(unique_smiles_).to_topology()
+            )
+
+            with open(pathlib.Path(self.base_dir) / "interchanges" / f"interchange_{index}.json", "w") as f:
+                f.write(interchange.model_dump_json())
 
         return [
             result

From 6f9aa3191d36b3c4a74edeefec9bc4e52eaf6001 Mon Sep 17 00:00:00 2001
From: "Matthew W. Thompson" <mattwthompson@protonmail.com>
Date: Wed, 30 Sep 2026 17:03:49 -0500
Subject: [PATCH 15/20] Stage new design

---
 tyff/compute/_jacobian.py | 57 ++++++++++++++++++++++++++++++++++-----
 1 file changed, 50 insertions(+), 7 deletions(-)

diff --git a/tyff/compute/_jacobian.py b/tyff/compute/_jacobian.py
index 31fe360..b9e47fd 100644
--- a/tyff/compute/_jacobian.py
+++ b/tyff/compute/_jacobian.py
@@ -1,5 +1,6 @@
 """Compute the ensemble average(s) and Jacobian matrix associated with a job."""
 
+import dataclasses
 import glob
 import json
 import pathlib
@@ -7,13 +8,44 @@
 import torch
 from openff.interchange import Interchange
 
+import tyff
+from tyff._serialization import load_tensor_force_field
 from tyff.compute._files import ProductionFiles
 from tyff.configs.liquid import BulkLiquid
 
 
+def _gather_from_reference(local: tyff.TensorForceField, reference: tyff.TensorForceField) -> tyff.TensorForceField:
+    """Rebuild ``local`` with values indexed out of ``reference``, keeping local row order so the
+    topologies' parameter maps stay valid."""
+    reference_by_type = reference.potentials_by_type
+    potentials = []
+
+    for potential in local.potentials:
+        ref = reference_by_type[potential.type]  # KeyError: potential type missing from reference
+        assert potential.parameter_cols == ref.parameter_cols, potential.type
+
+        ref_idx = {key: i for i, key in enumerate(ref.parameter_keys)}
+        idx = torch.tensor([ref_idx[key] for key in potential.parameter_keys])  # KeyError: unseen parameter
+
+        attributes = potential.attributes
+        if potential.attribute_cols is not None:
+            attr_idx = torch.tensor([ref.attribute_cols.index(col) for col in potential.attribute_cols])
+            attributes = ref.attributes[attr_idx]
+
+        potentials.append(dataclasses.replace(potential, parameters=ref.parameters[idx], attributes=attributes))
+
+    v_sites = local.v_sites
+    if v_sites is not None:
+        ref_idx = {key: i for i, key in enumerate(reference.v_sites.keys)}
+        idx = torch.tensor([ref_idx[key] for key in v_sites.keys])
+        v_sites = dataclasses.replace(v_sites, parameters=reference.v_sites.parameters[idx])
+
+    return tyff.TensorForceField(potentials, v_sites)
+
+
 def _get_ensemble_average_and_jacobian(
     production_future: dict[str, ProductionFiles],
-    interchanges_path: str | pathlib.Path,
+    force_field_path: str | pathlib.Path,
     job_dir: str,
 ) -> tuple[dict[str, torch.Tensor], torch.Tensor]:
     import openmm.unit
@@ -41,7 +73,7 @@ def _get_ensemble_average_and_jacobian(
     pressure = compute_config.get("pressure")  # atmosphere, float | None
 
     interchanges = []
-    for path_index, interchange_path in enumerate(sorted(glob.glob(f"{interchanges_path}/interchange_*.json"))):
+    for path_index, interchange_path in enumerate(sorted(glob.glob(f"{job_dir}/single_molecule_interchange_*.json"))):
         unique_molecule_index = int(pathlib.Path(interchange_path).stem.split("interchange_")[-1])
 
         # hope we're loading up the single-molecule interchanges in the same order as we have unique molecules
@@ -51,9 +83,16 @@ def _get_ensemble_average_and_jacobian(
             interchanges.append(Interchange.model_validate_json(f.read()))
 
     assert len(interchanges) > 0, "Did not find single-molecule `Interchange`s as expected"
+    assert len(interchanges) == len(compute_config["smiles"]) > 0, (
+        "Did not find same number of single-molecule `Interchange`s as number of unique molecules in compute config"
+    )
 
-    tensor_force_field, tensor_topologies = tyff.converters.convert_interchange(interchanges)
+    # make this argument just the "local" interchanges
+    local_force_field, tensor_topologies = tyff.converters.convert_interchange(interchanges)
 
+    # before this job (before ANY job ... ) a "global" tensor force field representing the entire data set
+    # needs to be created and serialized to somewhere root-like, accessible to all jobs
+    reference_force_field = load_tensor_force_field(f"{force_field_path}/reference.json")
     # must sync this up with tyff/compute/_pack.py if ever either change
     n_molecules = compute_config["n_molecules"]
     # also hope ordering lines up
@@ -68,23 +107,26 @@ def _get_ensemble_average_and_jacobian(
     frames_path = pathlib.Path(f"{job_dir}/production_trajectory.msgpack")
 
     # Use existing tyff packing order, including attributes and optional v-sites.
-    tensors, parameter_lookup, attribute_lookup, has_v_sites = _pack_force_field(tensor_force_field)
+    tensors, parameter_lookup, attribute_lookup, has_v_sites = _pack_force_field(reference_force_field)
 
     assert tensors is not None and len(tensors) > 0
 
     parameters = torch.cat([t.detach().reshape(-1) for t in tensors if t is not None]).requires_grad_(True)
     pieces = iter(parameters.split([t.numel() for t in tensors if t is not None]))
     tensors = tuple(None if t is None else next(pieces).reshape(t.shape) for t in tensors)
-    worker_ff = _unpack_force_field(
+    reference_worker_force_field = _unpack_force_field(
         tensors,
         parameter_lookup,
         attribute_lookup,
         has_v_sites,
-        tensor_force_field,
+        reference_force_field,
     )
+
+    worker_force_field = _gather_from_reference(local_force_field, reference_worker_force_field)
+
     means, _ = tyff.mm.compute_ensemble_averages(
         system,
-        worker_ff,
+        worker_force_field,
         frames_path,
         temperature * openmm.unit.kelvin,
         None if pressure is None else pressure * openmm.unit.atmosphere,
@@ -105,4 +147,5 @@ def _get_ensemble_average_and_jacobian(
     with open(f"{job_dir}/jacobian.pt", "wb") as f:
         torch.save(jacobian.detach(), f)
 
+    # jacobian should be shaped to "global"/reference force field, not local worker-scale
     return {name: value.detach() for name, value in means.items()}, jacobian.detach()

From 95f8afa5cdc3719ab188c3c4b23bf337fd892e5e Mon Sep 17 00:00:00 2001
From: "Matthew W. Thompson" <mattwthompson@protonmail.com>
Date: Wed, 30 Sep 2026 17:13:33 -0500
Subject: [PATCH 16/20] Create "reference" tensor force field representing all
 systems

---
 tyff/compute/_jacobian.py |  5 +++--
 tyff/compute/workflow.py  | 25 ++++++++++++++++++++++++-
 2 files changed, 27 insertions(+), 3 deletions(-)

diff --git a/tyff/compute/_jacobian.py b/tyff/compute/_jacobian.py
index b9e47fd..91762d8 100644
--- a/tyff/compute/_jacobian.py
+++ b/tyff/compute/_jacobian.py
@@ -45,8 +45,8 @@ def _gather_from_reference(local: tyff.TensorForceField, reference: tyff.TensorF
 
 def _get_ensemble_average_and_jacobian(
     production_future: dict[str, ProductionFiles],
-    force_field_path: str | pathlib.Path,
     job_dir: str,
+    reference_force_field=str | pathlib.Path,
 ) -> tuple[dict[str, torch.Tensor], torch.Tensor]:
     import openmm.unit
     import torch
@@ -92,7 +92,8 @@ def _get_ensemble_average_and_jacobian(
 
     # before this job (before ANY job ... ) a "global" tensor force field representing the entire data set
     # needs to be created and serialized to somewhere root-like, accessible to all jobs
-    reference_force_field = load_tensor_force_field(f"{force_field_path}/reference.json")
+    reference_force_field = load_tensor_force_field(reference_force_field)
+
     # must sync this up with tyff/compute/_pack.py if ever either change
     n_molecules = compute_config["n_molecules"]
     # also hope ordering lines up
diff --git a/tyff/compute/workflow.py b/tyff/compute/workflow.py
index 04a990a..be356ab 100644
--- a/tyff/compute/workflow.py
+++ b/tyff/compute/workflow.py
@@ -9,6 +9,7 @@
 from parsl import File
 from rich import print
 
+import tyff.converters
 from tyff.compute._files import (
     ProductionFiles,
 )
@@ -84,6 +85,8 @@ def submit_target(
         n_molecules: int,
         n_replicates: int = 3,
     ):
+        # TODO: Rename?
+        """Submit only one target. Not meant to be called by user, instead use `submit_target_batch`."""
 
         from tyff.compute.prep import (
             _compute_configs_from_data_entry,
@@ -122,23 +125,39 @@ def submit_target_batch(
     ):
         import openff.toolkit
 
+        from tyff._serialization import dump_tensor_force_field
+
         unique_smiles = set()
 
         for target in target_configs:
             for smiles_ in target["smiles"]:
                 unique_smiles.add(smiles_)
 
+        # TODO: Probably drop this
         (pathlib.Path(self.base_dir) / "interchanges").mkdir(exist_ok=True)
 
+        interchanges = list()
+
         force_field_ = openff.toolkit.ForceField(force_field)
         for index, unique_smiles_ in enumerate(unique_smiles):
             interchange = force_field_.create_interchange(
                 openff.toolkit.Molecule.from_smiles(unique_smiles_).to_topology()
             )
+            interchanges.append(interchange)
 
             with open(pathlib.Path(self.base_dir) / "interchanges" / f"interchange_{index}.json", "w") as f:
                 f.write(interchange.model_dump_json())
 
+        (pathlib.Path(self.base_dir) / "reference_force_field").mkdir(exist_ok=True)
+
+        # this is a "global"/reference force field composed from all unique molecules,
+        # don't think we're going to use the topologies here
+        reference_force_field, _tensor_topologies = tyff.converters.convert_interchange(interchanges)
+
+        # dumps a JSON representation of the tensor representation of the SMIRNOFF force field
+        with open(pathlib.Path(self.base_dir) / "reference_force_fields" / f"{force_field}.json", "w") as f:
+            dump_tensor_force_field(reference_force_field, f)
+
         return [
             result
             for spec in target_configs
@@ -283,7 +302,11 @@ def _common_run(self, compute_config: BaseComputeConfig) -> tuple[dict[str, torc
             job_dir=job_dir,
         )
 
-        jacobian_future = create_jacobian(production_future=production_future, job_dir=job_dir)
+        jacobian_future = create_jacobian(
+            production_future=production_future,
+            job_dir=job_dir,
+            reference_force_field=(self.base_dir / "reference_force_fields" / f"{compute_config['force_field']}.json"),
+        )
 
         return {"job_id": job_id, "future": jacobian_future}
 

From 863c770fefdc4dac38dbeb282d81e796f8afda65 Mon Sep 17 00:00:00 2001
From: "Matthew W. Thompson" <mattwthompson@protonmail.com>
Date: Thu, 1 Oct 2026 09:19:36 -0500
Subject: [PATCH 17/20] Update comments

---
 tyff/compute/_jacobian.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tyff/compute/_jacobian.py b/tyff/compute/_jacobian.py
index 91762d8..42eb29e 100644
--- a/tyff/compute/_jacobian.py
+++ b/tyff/compute/_jacobian.py
@@ -87,7 +87,6 @@ def _get_ensemble_average_and_jacobian(
         "Did not find same number of single-molecule `Interchange`s as number of unique molecules in compute config"
     )
 
-    # make this argument just the "local" interchanges
     local_force_field, tensor_topologies = tyff.converters.convert_interchange(interchanges)
 
     # before this job (before ANY job ... ) a "global" tensor force field representing the entire data set
@@ -99,6 +98,7 @@ def _get_ensemble_average_and_jacobian(
     # also hope ordering lines up
     n_copies = [int(n_molecules * x) for x in compute_config["x"]]
 
+    # this is on the scale of just this job
     system = tyff.TensorSystem(
         topologies=tensor_topologies,
         n_copies=n_copies,

From 538c6fd4896459239d56bd72387a118db7e3c40c Mon Sep 17 00:00:00 2001
From: "Matthew W. Thompson" <mattwthompson@protonmail.com>
Date: Thu, 1 Oct 2026 12:17:23 -0500
Subject: [PATCH 18/20] Improve handling "reference" force fields

---
 tyff/compute/workflow.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/tyff/compute/workflow.py b/tyff/compute/workflow.py
index be356ab..07a925d 100644
--- a/tyff/compute/workflow.py
+++ b/tyff/compute/workflow.py
@@ -148,15 +148,16 @@ def submit_target_batch(
             with open(pathlib.Path(self.base_dir) / "interchanges" / f"interchange_{index}.json", "w") as f:
                 f.write(interchange.model_dump_json())
 
-        (pathlib.Path(self.base_dir) / "reference_force_field").mkdir(exist_ok=True)
+        self._reference_force_fields = pathlib.Path(self.base_dir) / "reference_force_fields"
+        pathlib.Path(self._reference_force_fields).mkdir(exist_ok=True)
 
         # this is a "global"/reference force field composed from all unique molecules,
         # don't think we're going to use the topologies here
         reference_force_field, _tensor_topologies = tyff.converters.convert_interchange(interchanges)
 
         # dumps a JSON representation of the tensor representation of the SMIRNOFF force field
-        with open(pathlib.Path(self.base_dir) / "reference_force_fields" / f"{force_field}.json", "w") as f:
-            dump_tensor_force_field(reference_force_field, f)
+        with open(self._reference_force_fields / f"{force_field}.json", "w") as f:
+            json.dump(dump_tensor_force_field(reference_force_field), f)
 
         return [
             result
@@ -305,7 +306,7 @@ def _common_run(self, compute_config: BaseComputeConfig) -> tuple[dict[str, torc
         jacobian_future = create_jacobian(
             production_future=production_future,
             job_dir=job_dir,
-            reference_force_field=(self.base_dir / "reference_force_fields" / f"{compute_config['force_field']}.json"),
+            reference_force_field=self._reference_force_fields / f"{compute_config['force_field']}.json",
         )
 
         return {"job_id": job_id, "future": jacobian_future}

From fc8066f4c775606e12746825a4ff82fd6a5c6132 Mon Sep 17 00:00:00 2001
From: "Matthew W. Thompson" <mattwthompson@protonmail.com>
Date: Thu, 1 Oct 2026 13:32:39 -0500
Subject: [PATCH 19/20] Fixes

---
 tyff/compute/_jacobian.py | 4 ++--
 tyff/compute/apps.py      | 9 ++++++---
 tyff/compute/workflow.py  | 4 ++--
 3 files changed, 10 insertions(+), 7 deletions(-)

diff --git a/tyff/compute/_jacobian.py b/tyff/compute/_jacobian.py
index 42eb29e..d309398 100644
--- a/tyff/compute/_jacobian.py
+++ b/tyff/compute/_jacobian.py
@@ -46,7 +46,7 @@ def _gather_from_reference(local: tyff.TensorForceField, reference: tyff.TensorF
 def _get_ensemble_average_and_jacobian(
     production_future: dict[str, ProductionFiles],
     job_dir: str,
-    reference_force_field=str | pathlib.Path,
+    reference_force_field_path: str,
 ) -> tuple[dict[str, torch.Tensor], torch.Tensor]:
     import openmm.unit
     import torch
@@ -91,7 +91,7 @@ def _get_ensemble_average_and_jacobian(
 
     # before this job (before ANY job ... ) a "global" tensor force field representing the entire data set
     # needs to be created and serialized to somewhere root-like, accessible to all jobs
-    reference_force_field = load_tensor_force_field(reference_force_field)
+    reference_force_field = load_tensor_force_field(json.loads(open(reference_force_field_path).read()))
 
     # must sync this up with tyff/compute/_pack.py if ever either change
     n_molecules = compute_config["n_molecules"]
diff --git a/tyff/compute/apps.py b/tyff/compute/apps.py
index d0f349d..956589d 100644
--- a/tyff/compute/apps.py
+++ b/tyff/compute/apps.py
@@ -88,9 +88,12 @@ def run_dhvap_analysis(
 
 @python_app
 def create_jacobian(
-    production_future: dict[str, ProductionFiles],
-    job_dir: str,
+    production_future: dict[str, ProductionFiles], job_dir: str, reference_force_field_path: str
 ) -> tuple[dict[str, torch.Tensor], torch.Tensor]:
     from tyff.compute._jacobian import _get_ensemble_average_and_jacobian
 
-    return _get_ensemble_average_and_jacobian(production_future, job_dir)
+    return _get_ensemble_average_and_jacobian(
+        production_future,
+        job_dir,
+        reference_force_field_path,
+    )
diff --git a/tyff/compute/workflow.py b/tyff/compute/workflow.py
index 07a925d..7df6179 100644
--- a/tyff/compute/workflow.py
+++ b/tyff/compute/workflow.py
@@ -31,7 +31,7 @@
 
 
 class SimulationWorkflow:
-    def __init__(self, base_dir, parsl_config):
+    def __init__(self, base_dir: str, parsl_config: parsl.Config):
         from tyff.compute._logging import _set_up_logger
 
         pathlib.Path(base_dir).mkdir(exist_ok=True)
@@ -306,7 +306,7 @@ def _common_run(self, compute_config: BaseComputeConfig) -> tuple[dict[str, torc
         jacobian_future = create_jacobian(
             production_future=production_future,
             job_dir=job_dir,
-            reference_force_field=self._reference_force_fields / f"{compute_config['force_field']}.json",
+            reference_force_field_path=self._reference_force_fields / f"{compute_config['force_field']}.json",
         )
 
         return {"job_id": job_id, "future": jacobian_future}

From e34f43aec963d5a7de91d841fa93a99b2a8482d6 Mon Sep 17 00:00:00 2001
From: "Matthew W. Thompson" <mattwthompson@protonmail.com>
Date: Thu, 1 Oct 2026 14:18:48 -0500
Subject: [PATCH 20/20] Add basic test

---
 tyff/_tests/compute/test_jacobians.py         |  47 ++
 .../app_files/make_sample_density_files.py    |  29 +-
 .../data/app_files/sample_density/ref.ff.json | 504 ++++++++++++++++++
 .../single_molecule_interchange_0.json        |   1 +
 .../single_molecule_interchange_1.json        |   1 +
 .../sample_density/target_config.json         |   2 +-
 6 files changed, 579 insertions(+), 5 deletions(-)
 create mode 100644 tyff/_tests/compute/test_jacobians.py
 create mode 100644 tyff/_tests/data/app_files/sample_density/ref.ff.json
 create mode 100644 tyff/_tests/data/app_files/sample_density/single_molecule_interchange_0.json
 create mode 100644 tyff/_tests/data/app_files/sample_density/single_molecule_interchange_1.json

diff --git a/tyff/_tests/compute/test_jacobians.py b/tyff/_tests/compute/test_jacobians.py
new file mode 100644
index 0000000..bfe493d
--- /dev/null
+++ b/tyff/_tests/compute/test_jacobians.py
@@ -0,0 +1,47 @@
+import shutil
+from importlib.resources import files
+
+import pytest
+from parsl import File
+
+from tyff.compute._files import ProductionFiles
+from tyff.compute._jacobian import _get_ensemble_average_and_jacobian
+
+
+@pytest.fixture
+def production_future() -> dict[str, ProductionFiles]:
+    return {
+        "simulation_files": ProductionFiles(
+            topology=File(files("tyff") / "_tests/data/app_files/sample_density/production_topology.pdb"),
+            dcd_trajectory=File(files("tyff") / "_tests/data/app_files/sample_density/production_trajectory.dcd"),
+            msgpack_trajectory=File(
+                files("tyff") / "_tests/data/app_files/sample_density/production_trajectory.msgpack"
+            ),
+            log=File(files("tyff") / "_tests/data/app_files/sample_density/produce.log"),
+            state_data=File(files("tyff") / "_tests/data/app_files/sample_density/production.csv"),
+            system=File(files("tyff") / "_tests/data/app_files/sample_density/production_system.xml"),
+            integrator=File(files("tyff") / "_tests/data/app_files/sample_density/production_integrator.xml"),
+            checkpoint=File(files("tyff") / "_tests/data/app_files/sample_density/production_checkpoint.chk"),
+        )
+    }
+
+
+def test_jacobian(production_future, tmp_path):
+    for file in [
+        "compute_config.json",
+        "production_trajectory.msgpack",
+        "single_molecule_interchange_0.json",
+        "single_molecule_interchange_1.json",
+    ]:
+        shutil.copy(
+            str(files("tyff") / f"_tests/data/app_files/sample_density/{file}"),
+            str(tmp_path / file),
+        )
+
+    averages, jacobians = _get_ensemble_average_and_jacobian(
+        production_future,
+        tmp_path,
+        reference_force_field_path=File(files("tyff") / "_tests/data/app_files/sample_density/ref.ff.json"),
+    )
+
+    assert len(averages) == jacobians.shape[0]
diff --git a/tyff/_tests/data/app_files/make_sample_density_files.py b/tyff/_tests/data/app_files/make_sample_density_files.py
index bc2c6ec..c1c78c2 100644
--- a/tyff/_tests/data/app_files/make_sample_density_files.py
+++ b/tyff/_tests/data/app_files/make_sample_density_files.py
@@ -2,8 +2,11 @@
 import pathlib
 import random
 
+import openff.toolkit
 from rich.pretty import pprint as print
 
+import tyff.converters
+from tyff._serialization import dump_tensor_force_field
 from tyff.compute._analyze import _run_density_analysis
 from tyff.compute._equilibrate import _run_equilibration
 from tyff.compute._minimize import _minimize_energy
@@ -15,9 +18,11 @@
 
 pathlib.Path("sample_density/").mkdir(exist_ok=True)
 
+FORCE_FIELD = "openff-2.3.0.offxml"
+
 target = DataEntry(
     **{
-        "id": random.randint(10**15, 10**16 - 1),
+        "id": random.randint(10**15, 10**16 - 1),  # TODO: Make this the real ID
         "tag": "density",
         "x": [0.5, 0.5],
         "smiles": [
@@ -33,12 +38,28 @@
     }
 )
 
-with open("sample_density/target_config.json", "w") as target_config:
-    json.dump(target, target_config, indent=4)
+with open("sample_density/target_config.json", "w") as target_config_file:
+    json.dump(target, target_config_file, indent=4)
+
+smirnoff_force_field = openff.toolkit.ForceField(FORCE_FIELD)
+
+interchanges = [
+    smirnoff_force_field.create_interchange(openff.toolkit.Molecule.from_smiles(smiles_).to_topology())
+    for smiles_ in target["smiles"]
+]
+
+for index, interchange in enumerate(interchanges):
+    with open(f"sample_density/single_molecule_interchange_{index}.json", "w") as f:
+        f.write(interchange.model_dump_json())
+
+reference_force_field, _ = tyff.converters.convert_interchange(interchanges)
+
+with open("sample_density/ref.ff.json", "w") as reference_force_field_file:
+    json.dump(dump_tensor_force_field(reference_force_field), reference_force_field_file, indent=4)
 
 compute = _make_liquid_density_compute_configs(
     data_entry=target,
-    force_field="openff-2.3.0.offxml",
+    force_field=FORCE_FIELD,
     n_molecules=200,
 )[0][0]
 
diff --git a/tyff/_tests/data/app_files/sample_density/ref.ff.json b/tyff/_tests/data/app_files/sample_density/ref.ff.json
new file mode 100644
index 0000000..bdad4d0
--- /dev/null
+++ b/tyff/_tests/data/app_files/sample_density/ref.ff.json
@@ -0,0 +1,504 @@
+{
+    "potentials": [
+        {
+            "type": "Angles",
+            "fn": "k/2*(theta-angle)**2",
+            "parameters": {
+                "dtype": "float64",
+                "shape": [
+                    3,
+                    2
+                ],
+                "data": [
+                    208.3323698809,
+                    1.8967821629564474,
+                    99.69215181429,
+                    1.9137632227721684,
+                    39.43190954937,
+                    1.8850428776126391
+                ]
+            },
+            "parameter_keys": [
+                {
+                    "id": "[#1:1]-[#8:2]-[*:3]",
+                    "mult": null,
+                    "associated_handler": "Angles",
+                    "bond_order": null,
+                    "virtual_site_type": null,
+                    "cosmetic_attributes": {}
+                },
+                {
+                    "id": "[*:1]~[#6X4:2]-[*:3]",
+                    "mult": null,
+                    "associated_handler": "Angles",
+                    "bond_order": null,
+                    "virtual_site_type": null,
+                    "cosmetic_attributes": {}
+                },
+                {
+                    "id": "[#1:1]-[#6X4:2]-[#1:3]",
+                    "mult": null,
+                    "associated_handler": "Angles",
+                    "bond_order": null,
+                    "virtual_site_type": null,
+                    "cosmetic_attributes": {}
+                }
+            ],
+            "parameter_cols": [
+                "k",
+                "angle"
+            ],
+            "parameter_units": [
+                "kilocalorie / mole / radian ** 2",
+                "radian"
+            ],
+            "attributes": null,
+            "attribute_cols": null,
+            "attribute_units": null,
+            "exceptions": null
+        },
+        {
+            "type": "Bonds",
+            "fn": "k/2*(r-length)**2",
+            "parameters": {
+                "dtype": "float64",
+                "shape": [
+                    4,
+                    2
+                ],
+                "data": [
+                    1141.303841414,
+                    0.9711625418245,
+                    457.9258198725,
+                    1.525970013793,
+                    680.7664447835,
+                    1.092445809108,
+                    394.308606076,
+                    1.422597694743
+                ]
+            },
+            "parameter_keys": [
+                {
+                    "id": "[#8:1]-[#1:2]",
+                    "mult": null,
+                    "associated_handler": "Bonds",
+                    "bond_order": null,
+                    "virtual_site_type": null,
+                    "cosmetic_attributes": {}
+                },
+                {
+                    "id": "[#6X4:1]-[#6X4:2]",
+                    "mult": null,
+                    "associated_handler": "Bonds",
+                    "bond_order": null,
+                    "virtual_site_type": null,
+                    "cosmetic_attributes": {}
+                },
+                {
+                    "id": "[#6X4:1]-[#1:2]",
+                    "mult": null,
+                    "associated_handler": "Bonds",
+                    "bond_order": null,
+                    "virtual_site_type": null,
+                    "cosmetic_attributes": {}
+                },
+                {
+                    "id": "[#6:1]-[#8:2]",
+                    "mult": null,
+                    "associated_handler": "Bonds",
+                    "bond_order": null,
+                    "virtual_site_type": null,
+                    "cosmetic_attributes": {}
+                }
+            ],
+            "parameter_cols": [
+                "k",
+                "length"
+            ],
+            "parameter_units": [
+                "kilocalorie / angstrom ** 2 / mole",
+                "angstrom"
+            ],
+            "attributes": null,
+            "attribute_cols": null,
+            "attribute_units": null,
+            "exceptions": null
+        },
+        {
+            "type": "Electrostatics",
+            "fn": "coul",
+            "parameters": {
+                "dtype": "float64",
+                "shape": [
+                    11,
+                    1
+                ],
+                "data": [
+                    -0.834,
+                    0.417,
+                    -0.09628999854127566,
+                    0.1324500006934007,
+                    -0.6029300081233183,
+                    0.3982599986096223,
+                    0.01728000243504842,
+                    0.01728000243504842,
+                    0.044650000830491386,
+                    0.044650000830491386,
+                    0.044650000830491386
+                ]
+            },
+            "parameter_keys": [
+                {
+                    "id": "[#1]-[#8X2H2+0:1]-[#1]",
+                    "mult": 0,
+                    "associated_handler": "LibraryCharges",
+                    "bond_order": null,
+                    "virtual_site_type": null,
+                    "cosmetic_attributes": {}
+                },
+                {
+                    "id": "[#1:1]-[#8X2H2+0]-[#1]",
+                    "mult": 0,
+                    "associated_handler": "LibraryCharges",
+                    "bond_order": null,
+                    "virtual_site_type": null,
+                    "cosmetic_attributes": {}
+                },
+                {
+                    "id": "[H:4][C:1]([H:5])([H:6])[C:2]([H:7])([H:8])[O:3][H:9]",
+                    "mult": 0,
+                    "associated_handler": "NAGLChargesHandler",
+                    "bond_order": null,
+                    "virtual_site_type": null,
+                    "cosmetic_attributes": {}
+                },
+                {
+                    "id": "[H:4][C:1]([H:5])([H:6])[C:2]([H:7])([H:8])[O:3][H:9]",
+                    "mult": 1,
+                    "associated_handler": "NAGLChargesHandler",
+                    "bond_order": null,
+                    "virtual_site_type": null,
+                    "cosmetic_attributes": {}
+                },
+                {
+                    "id": "[H:4][C:1]([H:5])([H:6])[C:2]([H:7])([H:8])[O:3][H:9]",
+                    "mult": 2,
+                    "associated_handler": "NAGLChargesHandler",
+                    "bond_order": null,
+                    "virtual_site_type": null,
+                    "cosmetic_attributes": {}
+                },
+                {
+                    "id": "[H:4][C:1]([H:5])([H:6])[C:2]([H:7])([H:8])[O:3][H:9]",
+                    "mult": 3,
+                    "associated_handler": "NAGLChargesHandler",
+                    "bond_order": null,
+                    "virtual_site_type": null,
+                    "cosmetic_attributes": {}
+                },
+                {
+                    "id": "[H:4][C:1]([H:5])([H:6])[C:2]([H:7])([H:8])[O:3][H:9]",
+                    "mult": 4,
+                    "associated_handler": "NAGLChargesHandler",
+                    "bond_order": null,
+                    "virtual_site_type": null,
+                    "cosmetic_attributes": {}
+                },
+                {
+                    "id": "[H:4][C:1]([H:5])([H:6])[C:2]([H:7])([H:8])[O:3][H:9]",
+                    "mult": 5,
+                    "associated_handler": "NAGLChargesHandler",
+                    "bond_order": null,
+                    "virtual_site_type": null,
+                    "cosmetic_attributes": {}
+                },
+                {
+                    "id": "[H:4][C:1]([H:5])([H:6])[C:2]([H:7])([H:8])[O:3][H:9]",
+                    "mult": 6,
+                    "associated_handler": "NAGLChargesHandler",
+                    "bond_order": null,
+                    "virtual_site_type": null,
+                    "cosmetic_attributes": {}
+                },
+                {
+                    "id": "[H:4][C:1]([H:5])([H:6])[C:2]([H:7])([H:8])[O:3][H:9]",
+                    "mult": 7,
+                    "associated_handler": "NAGLChargesHandler",
+                    "bond_order": null,
+                    "virtual_site_type": null,
+                    "cosmetic_attributes": {}
+                },
+                {
+                    "id": "[H:4][C:1]([H:5])([H:6])[C:2]([H:7])([H:8])[O:3][H:9]",
+                    "mult": 8,
+                    "associated_handler": "NAGLChargesHandler",
+                    "bond_order": null,
+                    "virtual_site_type": null,
+                    "cosmetic_attributes": {}
+                }
+            ],
+            "parameter_cols": [
+                "charge"
+            ],
+            "parameter_units": [
+                "elementary_charge"
+            ],
+            "attributes": {
+                "dtype": "float64",
+                "shape": [
+                    5
+                ],
+                "data": [
+                    0.0,
+                    0.0,
+                    0.8333333333,
+                    1.0,
+                    9.0
+                ]
+            },
+            "attribute_cols": [
+                "scale_12",
+                "scale_13",
+                "scale_14",
+                "scale_15",
+                "cutoff"
+            ],
+            "attribute_units": [
+                "dimensionless",
+                "dimensionless",
+                "dimensionless",
+                "dimensionless",
+                "angstrom"
+            ],
+            "exceptions": null
+        },
+        {
+            "type": "ProperTorsions",
+            "fn": "k*(1+cos(periodicity*theta-phase))",
+            "parameters": {
+                "dtype": "float64",
+                "shape": [
+                    6,
+                    4
+                ],
+                "data": [
+                    0.4017369753905,
+                    3.0,
+                    0.0,
+                    1.0,
+                    0.2405111530649,
+                    1.0,
+                    0.0,
+                    1.0,
+                    0.1007185121278,
+                    3.0,
+                    0.0,
+                    1.0,
+                    0.4345168281108,
+                    1.0,
+                    0.0,
+                    1.0,
+                    1.270955113618,
+                    3.0,
+                    0.0,
+                    3.0,
+                    0.2392094731239,
+                    3.0,
+                    0.0,
+                    1.0
+                ]
+            },
+            "parameter_keys": [
+                {
+                    "id": "[#6X4:1]-[#6X4:2]-[#8X2H1:3]-[#1:4]",
+                    "mult": 0,
+                    "associated_handler": "ProperTorsions",
+                    "bond_order": null,
+                    "virtual_site_type": null,
+                    "cosmetic_attributes": {}
+                },
+                {
+                    "id": "[#6X4:1]-[#6X4:2]-[#8X2H1:3]-[#1:4]",
+                    "mult": 1,
+                    "associated_handler": "ProperTorsions",
+                    "bond_order": null,
+                    "virtual_site_type": null,
+                    "cosmetic_attributes": {}
+                },
+                {
+                    "id": "[#1:1]-[#6X4:2]-[#6X4:3]-[#8X2:4]",
+                    "mult": 0,
+                    "associated_handler": "ProperTorsions",
+                    "bond_order": null,
+                    "virtual_site_type": null,
+                    "cosmetic_attributes": {}
+                },
+                {
+                    "id": "[#1:1]-[#6X4:2]-[#6X4:3]-[#8X2:4]",
+                    "mult": 1,
+                    "associated_handler": "ProperTorsions",
+                    "bond_order": null,
+                    "virtual_site_type": null,
+                    "cosmetic_attributes": {}
+                },
+                {
+                    "id": "[*:1]-[#6X4:2]-[#8X2:3]-[#1:4]",
+                    "mult": 0,
+                    "associated_handler": "ProperTorsions",
+                    "bond_order": null,
+                    "virtual_site_type": null,
+                    "cosmetic_attributes": {}
+                },
+                {
+                    "id": "[#1:1]-[#6X4:2]-[#6X4:3]-[#1:4]",
+                    "mult": 0,
+                    "associated_handler": "ProperTorsions",
+                    "bond_order": null,
+                    "virtual_site_type": null,
+                    "cosmetic_attributes": {}
+                }
+            ],
+            "parameter_cols": [
+                "k",
+                "periodicity",
+                "phase",
+                "idivf"
+            ],
+            "parameter_units": [
+                "kilocalorie / mole",
+                "dimensionless",
+                "radian",
+                "dimensionless"
+            ],
+            "attributes": null,
+            "attribute_cols": null,
+            "attribute_units": null,
+            "exceptions": null
+        },
+        {
+            "type": "vdW",
+            "fn": "4*epsilon*((sigma/r)**12-(sigma/r)**6)",
+            "parameters": {
+                "dtype": "float64",
+                "shape": [
+                    7,
+                    2
+                ],
+                "data": [
+                    0.1521,
+                    3.1507,
+                    0.0,
+                    1.0,
+                    0.1205698919337,
+                    3.387971073428982,
+                    0.1353608645661,
+                    3.0237212943083334,
+                    1.232058709465e-05,
+                    0.5330964133900715,
+                    0.01891997418601,
+                    2.5586037672254887,
+                    0.01336628116185,
+                    2.6639341018377585
+                ]
+            },
+            "parameter_keys": [
+                {
+                    "id": "[#1]-[#8X2H2+0:1]-[#1]",
+                    "mult": null,
+                    "associated_handler": "vdW",
+                    "bond_order": null,
+                    "virtual_site_type": null,
+                    "cosmetic_attributes": {}
+                },
+                {
+                    "id": "[#1:1]-[#8X2H2+0]-[#1]",
+                    "mult": null,
+                    "associated_handler": "vdW",
+                    "bond_order": null,
+                    "virtual_site_type": null,
+                    "cosmetic_attributes": {}
+                },
+                {
+                    "id": "[#6X4:1]",
+                    "mult": null,
+                    "associated_handler": "vdW",
+                    "bond_order": null,
+                    "virtual_site_type": null,
+                    "cosmetic_attributes": {}
+                },
+                {
+                    "id": "[#8X2H1+0:1]",
+                    "mult": null,
+                    "associated_handler": "vdW",
+                    "bond_order": null,
+                    "virtual_site_type": null,
+                    "cosmetic_attributes": {}
+                },
+                {
+                    "id": "[#1:1]-[#8]",
+                    "mult": null,
+                    "associated_handler": "vdW",
+                    "bond_order": null,
+                    "virtual_site_type": null,
+                    "cosmetic_attributes": {}
+                },
+                {
+                    "id": "[#1:1]-[#6X4]-[#7,#8,#9,#16,#17,#35]",
+                    "mult": null,
+                    "associated_handler": "vdW",
+                    "bond_order": null,
+                    "virtual_site_type": null,
+                    "cosmetic_attributes": {}
+                },
+                {
+                    "id": "[#1:1]-[#6X4]",
+                    "mult": null,
+                    "associated_handler": "vdW",
+                    "bond_order": null,
+                    "virtual_site_type": null,
+                    "cosmetic_attributes": {}
+                }
+            ],
+            "parameter_cols": [
+                "epsilon",
+                "sigma"
+            ],
+            "parameter_units": [
+                "kilocalorie / mole",
+                "angstrom"
+            ],
+            "attributes": {
+                "dtype": "float64",
+                "shape": [
+                    6
+                ],
+                "data": [
+                    0.0,
+                    0.0,
+                    0.5,
+                    1.0,
+                    9.0,
+                    1.0
+                ]
+            },
+            "attribute_cols": [
+                "scale_12",
+                "scale_13",
+                "scale_14",
+                "scale_15",
+                "cutoff",
+                "switch_width"
+            ],
+            "attribute_units": [
+                "dimensionless",
+                "dimensionless",
+                "dimensionless",
+                "dimensionless",
+                "angstrom",
+                "angstrom"
+            ],
+            "exceptions": null
+        }
+    ],
+    "v_sites": null
+}
\ No newline at end of file
diff --git a/tyff/_tests/data/app_files/sample_density/single_molecule_interchange_0.json b/tyff/_tests/data/app_files/sample_density/single_molecule_interchange_0.json
new file mode 100644
index 0000000..597563a
--- /dev/null
+++ b/tyff/_tests/data/app_files/sample_density/single_molecule_interchange_0.json
@@ -0,0 +1 @@
+{"collections":{"Bonds":{"type":"Bonds","is_plugin":false,"expression":"k/2*(r-length)**2","key_map":{"{\"atom_indices\":[0,1],\"bond_order\":null}":"{\"id\":\"[#8:1]-[#1:2]\",\"mult\":null,\"associated_handler\":\"Bonds\",\"bond_order\":null,\"virtual_site_type\":null,\"cosmetic_attributes\":{}}","{\"atom_indices\":[0,2],\"bond_order\":null}":"{\"id\":\"[#8:1]-[#1:2]\",\"mult\":null,\"associated_handler\":\"Bonds\",\"bond_order\":null,\"virtual_site_type\":null,\"cosmetic_attributes\":{}}"},"potentials":{"{\"id\":\"[#8:1]-[#1:2]\",\"mult\":null,\"associated_handler\":\"Bonds\",\"bond_order\":null,\"virtual_site_type\":null,\"cosmetic_attributes\":{}}":"{\"parameters\":{\"k\":{\"val\":1141.303841414,\"unit\":\"kilocalorie_per_mole / angstrom ** 2\"},\"length\":{\"val\":0.9711625418245,\"unit\":\"angstrom\"}},\"map_key\":null}"},"fractional_bond_order_method":"AM1-Wiberg","fractional_bond_order_interpolation":"linear"},"Constraints":{"type":"Constraints","is_plugin":false,"expression":"","key_map":{"{\"atom_indices\":[0,1],\"bond_order\":null}":"{\"id\":\"[#1:1]-[#8X2H2+0:2]-[#1]\",\"mult\":null,\"associated_handler\":\"Constraints\",\"bond_order\":null,\"virtual_site_type\":null,\"cosmetic_attributes\":{}}","{\"atom_indices\":[0,2],\"bond_order\":null}":"{\"id\":\"[#1:1]-[#8X2H2+0:2]-[#1]\",\"mult\":null,\"associated_handler\":\"Constraints\",\"bond_order\":null,\"virtual_site_type\":null,\"cosmetic_attributes\":{}}","{\"atom_indices\":[1,2],\"bond_order\":null}":"{\"id\":\"[#1:1]-[#8X2H2+0]-[#1:2]\",\"mult\":null,\"associated_handler\":\"Constraints\",\"bond_order\":null,\"virtual_site_type\":null,\"cosmetic_attributes\":{}}"},"potentials":{"{\"id\":\"[#1:1]-[#8X2H2+0:2]-[#1]\",\"mult\":null,\"associated_handler\":\"Constraints\",\"bond_order\":null,\"virtual_site_type\":null,\"cosmetic_attributes\":{}}":"{\"parameters\":{\"distance\":{\"val\":0.9572,\"unit\":\"angstrom\"}},\"map_key\":null}","{\"id\":\"[#1:1]-[#8X2H2+0]-[#1:2]\",\"mult\":null,\"associated_handler\":\"Constraints\",\"bond_order\":null,\"virtual_site_type\":null,\"cosmetic_attributes\":{}}":"{\"parameters\":{\"distance\":{\"val\":1.5139006545247014,\"unit\":\"angstrom\"}},\"map_key\":null}"}},"Angles":{"type":"Angles","is_plugin":false,"expression":"k/2*(theta-angle)**2","key_map":{"{\"atom_indices\":[1,0,2]}":"{\"id\":\"[#1:1]-[#8:2]-[*:3]\",\"mult\":null,\"associated_handler\":\"Angles\",\"bond_order\":null,\"virtual_site_type\":null,\"cosmetic_attributes\":{}}"},"potentials":{"{\"id\":\"[#1:1]-[#8:2]-[*:3]\",\"mult\":null,\"associated_handler\":\"Angles\",\"bond_order\":null,\"virtual_site_type\":null,\"cosmetic_attributes\":{}}":"{\"parameters\":{\"k\":{\"val\":208.3323698809,\"unit\":\"kilocalorie_per_mole / radian ** 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diff --git a/tyff/_tests/data/app_files/sample_density/single_molecule_interchange_1.json b/tyff/_tests/data/app_files/sample_density/single_molecule_interchange_1.json
new file mode 100644
index 0000000..ad3d096
--- /dev/null
+++ b/tyff/_tests/data/app_files/sample_density/single_molecule_interchange_1.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/tyff/_tests/data/app_files/sample_density/target_config.json b/tyff/_tests/data/app_files/sample_density/target_config.json
index e49c583..e7a90ed 100644
--- a/tyff/_tests/data/app_files/sample_density/target_config.json
+++ b/tyff/_tests/data/app_files/sample_density/target_config.json
@@ -1,5 +1,5 @@
 {
-    "id": 3001675060647662,
+    "id": 2853385338953852,
     "tag": "density",
     "x": [
         0.5,