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name: schnetpack description: "Use SchNetPack for atomistic machine-learning datasets, Hydra training/prediction configs, neural network potential components, ASE/MD interfaces, and LAMMPS deployment guidance." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

SchNetPack Repo Skill

Use this skill when a task involves SchNetPack, atomistic neural networks, neural network potentials, molecular or materials datasets, SchNet/PaiNN-style models, ASE calculators, molecular dynamics, or SchNetPack command-line tools.

Fast Routing

• Use sub-skills/data-pipelines/SKILL.md for ASE DB datasets, unit metadata, property names, atomrefs, split files, AtomsDataModule, built-in datasets, and legacy dataset conversion.
• Use sub-skills/training-configs/SKILL.md for spktrain, spkpredict, Hydra config groups, experiment templates, trainer/logger/callback overrides, checkpoint behavior, and prediction commands.
• Use sub-skills/models-atomistic/SKILL.md for Python model construction, NeuralNetworkPotential, SchNet, PaiNN, output modules, force/stress response wiring, property keys, postprocessors, and transforms.
• Use sub-skills/interfaces-md/SKILL.md for trained-model runtime use through ASE calculators, ensemble uncertainty, spkmd, MD configs, TorchScript deployment, and LAMMPS pair-style guidance.

Read These Root References

• references/api-overview.md gives a compact map of SchNetPack modules, public object families, and verified signatures.
• references/cli-and-configuration.md summarizes installed command names, Hydra config families, safe help checks, and routing from CLI tasks to sub-skills.
• references/troubleshooting.md covers cross-cutting install/import, PyTorch/backend, CLI, Hydra, data, model, and MD failure surfaces.
• references/repo-provenance.md records the source snapshot and relative evidence paths used to generate this skill.
• references/repo-routing-metadata.json is structured metadata used by repo-skills-router during import.

Safe First Checks

Run these in the user's intended Python environment before deep debugging:

python scripts/schnetpack_import_check.py --json
python scripts/schnetpack_cli_check.py --commands spktrain spkpredict spkmd spkconvert spkdeploy

Use these scripts for import/signature/CLI availability checks only. They do not train models, download datasets, run MD, or build LAMMPS.

Install and Core Package Facts

Install in a normal Python environment with:

pip install schnetpack
python -c "import schnetpack as spk; print(spk.__version__)"

• Distribution/import module: schnetpack.
• Generated against SchNetPack version 2.2.0.
• Python requirement from package metadata: >=3.12.
• Primary dependencies include PyTorch, PyTorch Lightning, Hydra/OmegaConf, ASE, NumPy, SciPy, h5py, matscipy, tensorboard, and related utilities.
• Installed command names include spktrain, spkpredict, spkconvert, spkdeploy, and spkmd.

Workflow Map

User asks for	Start with	Then check
Create or repair an ASE DB, units, atomrefs, property lists, split files	data-pipelines	training-configs if the data feeds spktrain
Train QM9, MD17, rMD17, custom data, loggers, callbacks, checkpoints	training-configs	data-pipelines for schema errors; models-atomistic for output/task changes
Build/customize SchNet, PaiNN, force, stress, dipole, polarizability, postprocessors	models-atomistic	training-configs for Hydra command assembly
Use a trained model in ASE, relaxation, MD, ensemble uncertainty, LAMMPS	interfaces-md	models-atomistic if outputs or stress/force support are missing
Diagnose import, CLI, backend, or broad environment failures	references/troubleshooting.md	Nearest sub-skill troubleshooting file for workflow-specific errors

Safety Boundaries

• Do not launch long training, dataset downloads, GPU jobs, production MD, LAMMPS patching, or LAMMPS builds as routine validation.
• Prefer help/import/signature checks and tiny user-provided fixtures before real compute.
• Use CPU examples unless the user explicitly asks for CUDA and the environment verifies CUDA support.
• Keep native examples and tests as verification evidence, not runtime dependencies for this skill.
• When adapting user commands, make run/data/output directories explicit so Hydra's working-directory changes are predictable.

Common Decision Points

• If an ASE DB lacks _distance_unit or _property_unit_dict, route to data-pipelines before training or prediction.
• If a command uses model/representation=painn, that selects a config group; if it uses model.representation.n_interactions=5, that edits a field. Route syntax confusion to training-configs.
• If force or stress predictions fail, verify Forces, required_derivatives, property keys, and output/task wiring in models-atomistic before blaming ASE or MD.
• If spkmd fails on CPU-only hosts, check whether the MD config defaulted to device=cuda and add device=cpu when appropriate.
• If LAMMPS deployment is requested, use the bundled deployment helper and read the LAMMPS reference before proposing build or patch steps.