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name: models-atomistic description: "Build, inspect, customize, and troubleshoot SchNetPack atomistic neural network components in Python and Hydra configs." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

SchNetPack Models and Atomistic Components

Use this sub-skill when the task is about composing SchNetPack atomistic model objects, output modules, representation networks, response properties, radial/cutoff layers, postprocessors, or model/task key alignment.

Read First

• references/api-reference.md for constructor signatures, object contracts, and Python assembly patterns.
• references/model-components.md for choosing representations, input modules, output modules, response layers, and Hydra snippets.
• references/property-keys-and-units.md for tensor keys, target names, units, dtype, offsets, and postprocessor behavior.
• references/troubleshooting.md for common atomistic model failures and fixes.

Core Mental Model

SchNetPack atomistic models pass a mutable dictionary of tensors through input_modules, one representation, output_modules, and optional postprocessors. NeuralNetworkPotential collects required_derivatives and model_outputs from child modules, initializes requested gradients on input tensors, runs modules sequentially, applies postprocessors only when enabled, and returns only the collected output keys.

Route Boundaries

• Use this sub-skill for NeuralNetworkPotential, AtomisticTask, ModelOutput, SchNet, PaiNN, SO3net, FieldSchNet, GaussianRBF, BesselRBF, CosineCutoff, Atomwise, Forces, Response, DipoleMoment, Polarizability, Coulomb/ZBL priors, StaticExternalFields, PairwiseDistances, key mapping, and postprocessing.
• Route dataset creation, ASEAtomsData, AtomsDataModule, splitting, and dataset unit declarations to ../data-pipelines/SKILL.md.
• Route CLI/Hydra run assembly, experiment directory layout, trainers, loggers, checkpoint callbacks, and spktrain overrides to ../training-configs/SKILL.md.
• Route ASE calculators, AtomsConverter, SpkCalculator, MD, deployment, and LAMMPS integration to ../interfaces-md/SKILL.md.

Safe Working Pattern

1. Pick one property-key vocabulary and reuse it in model outputs, task targets, losses, metrics, data properties, and postprocessors.
2. Ensure neighbor-list transforms are present before PairwiseDistances; representation modules require _Rij, _idx_i, and _idx_j.
3. Add derivative modules such as Forces or Response as output modules, not task outputs alone.
4. Match every predicted output in model.output_modules with a task.outputs ModelOutput when it participates in supervised training.
5. Keep postprocessors for inference-scale outputs; AtomisticTask.predict_without_postprocessing() disables them while computing losses.

Avoid

• Do not run long training, downloads, GPU molecular dynamics, or native repository tests from this sub-skill.
• Do not reference local checkout paths or machine-specific environment details in generated configs.
• Do not put data-loading, CLI workflow, or calculator deployment details here beyond routing links.