diff --git a/dimsim/compute/__init__.py b/dimsim/compute/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/dimsim/compute/apps.py b/dimsim/compute/apps.py
new file mode 100644
index 0000000..47d5937
--- /dev/null
+++ b/dimsim/compute/apps.py
@@ -0,0 +1,182 @@
+import openmm
+from openff.toolkit import Topology
+from parsl.app.app import python_app
+
+from dimsim.configs.compute.density import DensityConfig
+
+
+@python_app
+def prepare_packed_topology(
+    compute_config: DensityConfig,
+    job_dir: str,
+) -> dict[str, DensityConfig | Topology]:
+    import logging
+    import pathlib
+    import time
+
+    from openff.packmol import pack_box
+    from openff.toolkit import Molecule, Quantity
+
+    # making the job dir should maybe happen inside of SimulationWorkflow.submit() instead of here?
+    pathlib.Path(job_dir).mkdir(exist_ok=True)
+
+    logging.basicConfig(
+        filename=f"{job_dir}/simulation.log",
+        level=logging.INFO,
+    )
+    logging.info("Starting packing app")
+
+    data_entry = compute_config["target"]
+    n_molecules = compute_config["n_molecules"]
+
+    time.sleep(n_molecules * 0.001)
+    filename = f"{job_dir}/packed_topology.pdb"
+
+    molecules = [Molecule.from_smiles(smiles) for smiles in data_entry["smiles"]]
+
+    if pathlib.Path(filename).exists():
+        logging.info(f"File {filename} already exists, skipping packing.")
+        return {
+            "compute_config": compute_config,
+            "packed_topology": Topology.from_pdb(filename, unique_molecules=molecules),
+        }
+
+    n_copies = [int(n_molecules * x) for x in data_entry["x"]]
+
+    result = pack_box(
+        molecules,
+        n_copies,
+        target_density=Quantity(data_entry["value"] * 0.7, "g/mL"),
+        working_directory=job_dir,
+    )
+
+    result.to_file(f"{job_dir}/packed_topology.pdb", file_format="pdb")
+
+    logging.info(f"packed {result.n_molecules} molecules")
+
+    return {
+        "compute_config": compute_config,
+        "packed_topology": result,
+    }
+
+
+@python_app
+def prepare_openmm_system(
+    packing_future: dict[str, DensityConfig | Topology],
+    job_dir: str,
+) -> dict[str, DensityConfig | openmm.System]:
+    import logging
+    import pathlib
+
+    import openmm
+    from openff.toolkit import ForceField
+
+    logging.basicConfig(
+        filename=f"{job_dir}/simulation.log",
+        level=logging.INFO,
+    )
+    logging.info("Starting OpenMM system creation app")
+
+    filename = f"{job_dir}/openmm_system.xml"
+
+    if pathlib.Path(filename).exists():
+        logging.warning(f"File {filename} already exists, skipping packing.")
+        return {
+            "compute_config": packing_future["compute_config"],
+            "openmm_system": openmm.XmlSerializer.deserialize(open(filename).read()),
+        }
+
+    compute_config = packing_future["compute_config"]
+    packed_topology: Topology = packing_future["packed_topology"]
+
+    force_field = ForceField(compute_config["force_field"])
+
+    openmm_system = force_field.create_openmm_system(packed_topology)
+
+    with open(filename, "w") as f:
+        f.write(openmm.XmlSerializer.serialize(openmm_system))
+
+    logging.info("made openmm system!")
+    return {
+        "compute_config": compute_config,
+        "openmm_system": openmm_system,
+    }
+
+
+@python_app
+def minimize_energy(
+    system_future: dict[str, DensityConfig | openmm.System], job_dir: str
+) -> dict[str, DensityConfig | float]:
+    import logging
+
+    import openmm
+    import openmm.app
+    import openmm.unit
+    from openff.toolkit import Molecule, Topology
+
+    logging.basicConfig(
+        filename=f"{job_dir}/simulation.log",
+        level=logging.INFO,
+    )
+    logging.info("Starting energy minimization app")
+    system = system_future["openmm_system"]
+
+    data_entry = system_future["compute_config"]["target"]
+
+    # this should be in Kelvin
+    temperature = system_future["compute_config"]["target"]["temperature"]
+
+    packed_topology_file = f"{job_dir}/packed_topology.pdb"
+    minimized_topology_file = f"{job_dir}/minimized_topology.pdb"
+
+    molecules = [Molecule.from_smiles(smiles) for smiles in data_entry["smiles"]]
+
+    topology = Topology.from_pdb(
+        packed_topology_file,
+        unique_molecules=molecules,
+    )
+
+    simulation = openmm.app.Simulation(
+        topology=topology.to_openmm(),
+        system=system,
+        integrator=openmm.LangevinMiddleIntegrator(
+            temperature * openmm.unit.kelvin,
+            1.0 / openmm.unit.picosecond,
+            1.0 * openmm.unit.femtoseconds,
+        ),
+    )
+
+    simulation.context.setPositions(topology.get_positions().to_openmm())
+
+    original_state = simulation.context.getState(energy=True)
+
+    simulation.minimizeEnergy()
+
+    # simulation.reporters.append(openmm.app.DCDReporter(outputs[0].filepath, 100))
+
+    # print("Minimized energy, now running 10,000 steps of dynamics...")
+    # simulation.step(10_000)
+
+    final_state = simulation.context.getState(energy=True, positions=True)
+
+    with open(minimized_topology_file, "w") as f:
+        openmm.app.PDBFile.writeFile(simulation.topology, final_state.getPositions(), f)
+
+    original = original_state.getPotentialEnergy().value_in_unit(openmm.unit.kilojoule_per_mole)
+    final = final_state.getPotentialEnergy().value_in_unit(openmm.unit.kilojoule_per_mole)
+
+    logging.info(f"Minimized energy from {original:.2f} to {final:.2f}")
+
+    return original, final
+
+
+@python_app
+def run_simulation(config, job_dir, steps=10000):
+    ...
+    return {"trajectory": f"{job_dir}/trajectory.dcd", "energy": -12345.6}
+
+
+@python_app
+def analyze_trajectory(simulation_result, job_dir):
+    ...
+    return {"mean_energy": ..., "rmsd": ...}
diff --git a/dimsim/compute/configs.py b/dimsim/compute/configs.py
new file mode 100644
index 0000000..a820120
--- /dev/null
+++ b/dimsim/compute/configs.py
@@ -0,0 +1,56 @@
+import os
+
+from parsl.config import Config
+from parsl.executors import HighThroughputExecutor, ThreadPoolExecutor
+from parsl.providers import LocalProvider, SlurmProvider
+from parsl.utils import get_all_checkpoints
+
+
+def local_config():
+    """For development and testing."""
+    if False:
+        # If openff-packmol/temporary_cd was threadsafe, better to use
+        # ThreadPoolExecutor and local config
+        return Config(
+            executors=[ThreadPoolExecutor(label="local")],
+            checkpoint_mode="task_exit",
+            checkpoint_files=get_all_checkpoints(),
+        )
+
+    return Config(
+        executors=[
+            HighThroughputExecutor(
+                label="local_process_pool",
+                # LocalProvider runs on the local machine
+                provider=LocalProvider(
+                    init_blocks=1,
+                    max_blocks=1,
+                ),
+                # Optional: Specify exact max workers (processes)
+                max_workers_per_node=int(os.cpu_count() / 2),
+            )
+        ],
+        strategy=None,  # Disable dynamic scaling for simpler local execution
+    )
+
+
+def slurm_config(partition, max_blocks=100):
+    """For production HPC runs."""
+    return Config(
+        executors=[
+            HighThroughputExecutor(
+                label="slurm_gpu",
+                provider=SlurmProvider(
+                    partition=partition,
+                    walltime="02:00:00",
+                    nodes_per_block=1,
+                    min_blocks=0,
+                    max_blocks=max_blocks,
+                    scheduler_options="#SBATCH --gres=gpu:1",
+                    worker_init="source activate myenv",
+                ),
+            )
+        ],
+        checkpoint_mode="task_exit",
+        checkpoint_files=get_all_checkpoints(),
+    )
diff --git a/dimsim/compute/jobs.py b/dimsim/compute/jobs.py
new file mode 100644
index 0000000..7202c08
--- /dev/null
+++ b/dimsim/compute/jobs.py
@@ -0,0 +1,35 @@
+import hashlib
+import json
+import os
+
+from dimsim.configs.compute.density import DensityConfig
+
+
+def make_job_id(compute_config: DensityConfig) -> str:
+    """
+    Generate a deterministic job ID from compute parameters.
+
+    Maybe this could be simplified if it only has one argument?
+    """
+    params = {
+        "tag": compute_config["tag"],
+        "target": compute_config["target"],
+        "force_field": compute_config["force_field"],
+        "n_molecules": str(compute_config["n_molecules"]),
+    }
+
+    return hashlib.sha256(json.dumps(params, sort_keys=True).encode()).hexdigest()
+
+
+def get_job_paths(base_dir, job_id):
+    return {
+        "root": f"{base_dir}/{job_id}",
+        "checkpoint": f"{base_dir}/{job_id}/checkpoint.chk",
+        "trajectory": f"{base_dir}/{job_id}/trajectory.dcd",
+        "log": f"{base_dir}/{job_id}/simulation.log",
+    }
+
+
+def is_complete(base_dir, job_id):
+    """Check if a job already has complete outputs."""
+    return os.path.exists(get_job_paths(base_dir, job_id)["trajectory"])
diff --git a/dimsim/compute/workflow.py b/dimsim/compute/workflow.py
new file mode 100644
index 0000000..d863521
--- /dev/null
+++ b/dimsim/compute/workflow.py
@@ -0,0 +1,77 @@
+import pathlib
+
+import parsl
+
+from dimsim.compute.apps import (
+    minimize_energy,
+    prepare_openmm_system,
+    prepare_packed_topology,
+)
+from dimsim.compute.jobs import get_job_paths, is_complete, make_job_id
+from dimsim.configs.compute.density import DensityConfig
+
+
+class SimulationWorkflow:
+    def __init__(self, base_dir, parsl_config):
+        pathlib.Path(base_dir).mkdir(exist_ok=True)
+
+        self.base_dir = base_dir
+
+        parsl.load(parsl_config)
+
+    def submit(self, compute_config: DensityConfig):
+        """Submit a single end-to-end simulation pipeline."""
+        job_id = make_job_id(compute_config)
+        job_dir = get_job_paths(self.base_dir, job_id)["root"]
+
+        if is_complete(self.base_dir, job_id):
+            return None  # already done, skip
+
+        # packed_pdb = File(f"{job_dir}/packed.pdb")
+        # system_xml = File(f"{job_dir}/system.xml")
+        # trajectory_dcd = File(f"{job_dir}/trajectory.dcd")
+
+        # 1. pack from compute config
+        pack_future = prepare_packed_topology(compute_config, job_dir)
+
+        # 2. set up openmm system
+        setup_future = prepare_openmm_system(pack_future, job_dir)
+
+        # 3 (for now ...) get minimized energy
+        minimize_future = minimize_energy(setup_future, job_dir)
+
+        # 3. run equilibration step
+        # 4. run "production" step
+        # sim_future = run_simulation(config_future, job_dir)
+        # 5. analyze trajectory
+        # 6. check for convergence, if not converged, run more production and repeat
+        # analysis_future = analyze_trajectory(sim_future, job_dir)
+
+        return {"job_id": job_id, "future": minimize_future}
+
+    def submit_batch(self, compute_configs: list[DensityConfig]):
+        """Submit many jobs, skipping already-complete ones."""
+        return [result for spec in compute_configs if (result := self.submit(spec)) is not None]
+
+    def run(self, compute_configs: list[DensityConfig]):
+        """Submit a batch and block until all complete."""
+        pending = self.submit_batch(compute_configs)
+
+        results = []
+        for item in pending:
+            try:
+                result = item["future"].result()
+                results.append({"job_id": item["job_id"], "result": result})
+            except Exception as e:
+                results.append({"job_id": item["job_id"], "error": str(e)})
+
+        return results
+
+    def shutdown(self):
+        parsl.clear()
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, *args):
+        self.shutdown()
diff --git a/dimsim/configs/compute/__init__.py b/dimsim/configs/compute/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/dimsim/configs/compute/density.py b/dimsim/configs/compute/density.py
new file mode 100644
index 0000000..b14c737
--- /dev/null
+++ b/dimsim/configs/compute/density.py
@@ -0,0 +1,17 @@
+import typing
+
+from dimsim.configs.targets.thermo import DataEntry
+
+
+class DensityConfig(typing.TypedDict):
+    tag: typing.Literal["density"] = "density"
+
+    target: DataEntry
+
+    force_field: str
+
+    n_molecules: int
+
+    """
+    Maybe add an RNG seed?
+    """
diff --git a/pixi.lock b/pixi.lock
index 9f6dc85..cc6520a 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -57,23 +57,23 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_9.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.6-hb03c661_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/c-blosc2-2.15.2-h3122c55_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.2.25-hbd8a1cb_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.4.22-hbd8a1cb_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cached-property-1.5.2-hd8ed1ab_1.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/cached_property-1.5.2-pyha770c72_1.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/cachetools-5.5.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.0-hbb29018_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2026.2.25-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2026.4.22-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-2.0.0-py312h460c074_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cftime-1.6.5-py312h4f23490_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.7-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.3.1-pyh8f84b5b_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.3.3-pyhc90fa1f_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/click-option-group-0.5.6-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cloudpickle-3.1.2-pyhcf101f3_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/comm-0.2.3-pyhe01879c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.3-py312h0a2e395_4.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.12.13-py312hd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cryptography-46.0.5-py312ha4b625e_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvrtc-13.2.51-hecca717_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cryptography-46.0.7-py312ha4b625e_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvrtc-13.2.78-hecca717_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-version-13.2-he2cc418_3.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhcf101f3_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cytoolz-1.1.0-py312h4c3975b_2.conda
@@ -91,8 +91,8 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/durationpy-0.10-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/executing-2.2.1-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/fftw-3.3.10-nompi_h3b011a4_112.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/filelock-3.25.2-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/fftw-3.3.11-nompi_h3b011a4_100.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/filelock-3.29.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/flexcache-0.3-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/flexparser-0.4-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2
@@ -102,7 +102,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.17.1-h27c8c51_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-hc364b38_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.62.0-py312h8a5da7c_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.62.1-py312h8a5da7c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/fqdn-1.5.1-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/freetype-2.14.3-ha770c72_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/freetype-py-2.3.0-pyhd8ed1ab_0.tar.bz2
@@ -112,8 +112,8 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/glog-0.7.1-hbabe93e_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/gmp-6.3.0-hac33072_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/gmpy2-2.3.0-py312hcaba1f9_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/google-auth-2.49.1-pyhcf101f3_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/greenlet-3.3.2-py312h8285ef7_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/google-auth-2.49.2-pyhcf101f3_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/greenlet-3.5.0-py312h8285ef7_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/h11-0.16.0-pyhcf101f3_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/h2-4.3.0-pyhcf101f3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/h5py-3.12.1-nompi_py312hd203070_103.conda
@@ -127,11 +127,11 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/huggingface_hub-0.36.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/hyperframe-6.1.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-73.2-h59595ed_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.11-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.13-pyhcf101f3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.8.0-pyhcf101f3_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.5.2-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/importlib_resources-7.1.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/ipykernel-7.2.0-pyha191276_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/ipython-9.12.0-pyhecfbec7_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/ipython-9.13.0-pyh53cf698_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/ipython_pygments_lexers-1.1.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/ipywidgets-8.1.8-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/iso8601-2.1.0-pyhd8ed1ab_1.conda
@@ -150,7 +150,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter-lsp-2.3.1-pyhcf101f3_0.conda
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+  md5: b8c47eab4e58fe7015a12ceb9d5b114c
   depends:
   - __osx >=11.0
-  - liblzma 5.8.2 h8088a28_0
+  - liblzma 5.8.3 h8088a28_0
   constrains:
-  - xz 5.8.2.*
+  - xz 5.8.3.*
   license: 0BSD AND LGPL-2.1-or-later
   purls: []
-  size: 85638
-  timestamp: 1768753028023
+  size: 85931
+  timestamp: 1775825857949
 - conda: https://conda.anaconda.org/conda-forge/linux-64/yaml-0.2.5-h280c20c_3.conda
   sha256: 6d9ea2f731e284e9316d95fa61869fe7bbba33df7929f82693c121022810f4ad
   md5: a77f85f77be52ff59391544bfe73390a
@@ -17731,7 +19098,7 @@ packages:
   license: Apache-2.0
   license_family: Apache
   purls:
-  - pkg:pypi/yarl?source=compressed-mapping
+  - pkg:pypi/yarl?source=hash-mapping
   size: 149070
   timestamp: 1772409506948
 - conda: https://conda.anaconda.org/conda-forge/linux-64/yarl-1.23.0-py312h8a5da7c_0.conda
@@ -17785,6 +19152,21 @@ packages:
   - pkg:pypi/yarl?source=hash-mapping
   size: 140208
   timestamp: 1772409657987
+- conda: https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.5-h387f397_9.conda
+  sha256: 47cfe31255b91b4a6fa0e9dbaf26baa60ac97e033402dbc8b90ba5fee5ffe184
+  md5: 8035e5b54c08429354d5d64027041cad
+  depends:
+  - libstdcxx >=14
+  - libgcc >=14
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=14
+  - libsodium >=1.0.20,<1.0.21.0a0
+  - krb5 >=1.21.3,<1.22.0a0
+  license: MPL-2.0
+  license_family: MOZILLA
+  purls: []
+  size: 310648
+  timestamp: 1757370847287
 - conda: https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.5-h41580af_10.conda
   sha256: 325d370b28e2b9cc1f765c5b4cdb394c91a5d958fbd15da1a14607a28fee09f6
   md5: 755b096086851e1193f3b10347415d7c
@@ -17812,6 +19194,19 @@ packages:
   purls: []
   size: 245246
   timestamp: 1772476886668
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zeromq-4.3.5-h888dc83_9.conda
+  sha256: b6f9c130646e5971f6cad708e1eee278f5c7eea3ca97ec2fdd36e7abb764a7b8
+  md5: 26f39dfe38a2a65437c29d69906a0f68
+  depends:
+  - __osx >=11.0
+  - libcxx >=19
+  - libsodium >=1.0.20,<1.0.21.0a0
+  - krb5 >=1.21.3,<1.22.0a0
+  license: MPL-2.0
+  license_family: MOZILLA
+  purls: []
+  size: 244772
+  timestamp: 1757371008525
 - conda: https://conda.anaconda.org/conda-forge/noarch/zict-3.0.0-pyhd8ed1ab_1.conda
   sha256: 5488542dceeb9f2874e726646548ecc5608060934d6f9ceaa7c6a48c61f9cc8d
   md5: e52c2ef711ccf31bb7f70ca87d144b9e
@@ -17823,18 +19218,18 @@ packages:
   - pkg:pypi/zict?source=hash-mapping
   size: 36341
   timestamp: 1733261642963
-- conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda
-  sha256: b4533f7d9efc976511a73ef7d4a2473406d7f4c750884be8e8620b0ce70f4dae
-  md5: 30cd29cb87d819caead4d55184c1d115
+- conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.1-pyhcf101f3_0.conda
+  sha256: 523616c0530d305d2216c2b4a8dfd3872628b60083255b89c5e0d8c42e738cca
+  md5: e1c36c6121a7c9c76f2f148f1e83b983
   depends:
   - python >=3.10
   - python
   license: MIT
   license_family: MIT
   purls:
-  - pkg:pypi/zipp?source=hash-mapping
-  size: 24194
-  timestamp: 1764460141901
+  - pkg:pypi/zipp?source=compressed-mapping
+  size: 24461
+  timestamp: 1776131454755
 - conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-1.3.2-h25fd6f3_2.conda
   sha256: 245c9ee8d688e23661b95e3c6dd7272ca936fabc03d423cdb3cdee1bbcf9f2f2
   md5: c2a01a08fc991620a74b32420e97868a
diff --git a/pixi.toml b/pixi.toml
index b0cc346..7409814 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -40,7 +40,12 @@ py312openeye = [ "py312", "openeye", "test", "typing" ]
 dev = [ "py312", "openeye", "test", "typing", "dev" ]
 
 [feature.dev.dependencies]
+openff-packmol = "0.1.*"
+pymol-open-source = "*"
+
+[feature.dev.pypi-dependencies]
 pre-commit = "*"
+parsl = "*"
 
 [feature.openeye]
 channels = [ "openeye", "conda-forge" ]