torchsim

name	torchsim
description	Comprehensive TorchSim / torch-sim atomistic simulation skill for molecular dynamics, relaxation, high-throughput MLIP workflows, trajectory generation, trajectory analysis, validation, and movie production for materials systems.

TorchSim Atomistic Simulation Skill

Use this skill when the user asks for atomistic molecular dynamics (MD), relaxation, static property evaluation, trajectory analysis, or visualization using TorchSim / torch-sim.

This skill targets the active atomistic simulation engine at https://github.com/torchsim/torch-sim, installed as torch-sim-atomistic and imported as torch_sim. Do not confuse it with the unrelated PyPI package torchsim for MRI simulation or the older GoodAI TorchSim project.

Core facts to preserve

• Package name: torch-sim-atomistic.
• Python import: import torch_sim as ts.
• Python requirement: Python >= 3.12.
• Core runners:
  • ts.integrate(...) for MD.
  • ts.optimize(...) for relaxation.
  • ts.static(...) for one-shot energy / force / stress-style evaluations.
• Core state object: ts.SimState, with tensor-native batched state.
• Trajectory writing/reading:
  • ts.TrajectoryReporter(...) for regular state/property reporting.
  • ts.TorchSimTrajectory(...) for HDF5 trajectory IO and analysis.
• Standard unit convention follows TorchSim's metal-style units: energy eV, length Angstrom, time ps, temperature K, pressure bar-style quantities unless explicitly transformed.
• Supported workflows include batched simulation, automatic GPU memory management/autobatching, ASE/pymatgen/phonopy interoperability, classical potentials, MLIPs, structural relaxation, elastic properties, MC/swap workflows, and differentiable simulations.

Skill contract

When using this skill for a user task, produce a complete, reproducible workflow. Do not only give prose. Depending on the request, create or provide:

1. A clear simulation protocol: model, system, ensemble, timestep, equilibration, production length, reporting cadence, and validation checks.
2. Runnable Python scripts or notebooks when appropriate.
3. Trajectory outputs in TorchSim HDF5 plus optional ASE .traj, .xyz, .extxyz, or .pdb exports.
4. Analysis outputs: CSV/JSON summaries, figures, and physical validation checks.
5. Movie/visualization outputs when requested: MP4/GIF plus raw frames or an OVITO/VMD/ASE-compatible trajectory.
6. A README explaining exact run commands and expected outputs.

Default workflow for MD tasks

Follow this order unless the user has a reason not to:

1. System construction/import

   • Read existing structures from CIF/POSCAR/XYZ/PDB/extxyz/traj if provided.
   • Otherwise construct with ASE/pymatgen: bulk crystals, surfaces/slabs, defects, interfaces, nanoparticles, liquids, polymers, or amorphous cells.
   • Make PBC explicit. Avoid accidental nonperiodic boxes.

2. Model selection

   • For realistic materials simulations, prefer a validated MLIP for the chemistry/conditions.
   • For demos/tests, use classical potentials such as Lennard-Jones, Morse, or soft-sphere.
   • Make model validity explicit: supported elements, pressure/temperature range, phase, charge/polarity, surfaces/defects/out-of-domain risks.

3. Relaxation / static precheck

   • Relax atomic positions before MD for solids and interfaces.
   • Relax cell only when physically appropriate and model supports stress/cell forces.
   • Run static energy/force sanity checks before a long MD.

4. Equilibration

   • Use NVT for thermal equilibration when volume is known.
   • Use NPT when density/lattice parameters should relax.
   • Choose timestep conservatively: typical MLIP solid/liquid MD often uses 0.5-2 fs = 0.0005-0.002 ps; reduce for H-rich systems, high T, reactive events, or stiff bonds.

5. Production

   • Save full state at a cadence that balances temporal resolution and file size.
   • Save scalar properties more frequently than full coordinates when needed.
   • Use separate trajectory files for batched systems.

6. Validation

   • Check energy drift for NVE.
   • Check target temperature in NVT.
   • Check pressure/volume stability in NPT.
   • Check force maxima, atom overlap, box collapse, model warnings, NaNs, and file completeness.

7. Analysis

   • Always include basic thermodynamics: potential energy, kinetic energy, total energy, temperature, pressure/volume if available.
   • Add material-specific analysis: RDF, MSD/diffusion, VACF, elastic tensor, stress-strain, heat flux/thermal conductivity, coordination, defects, surface/interface metrics, cluster statistics, polymer conformations, bond/angle/dihedral distributions.

8. Visualization

   • Export trajectories to standard formats for OVITO/VMD/ASE.

Routing

Consult the task files for deeper workflows:

• tasks/setup_environment.md: installation, dependency isolation, extras, GPU checks.
• tasks/model_selection.md: MLIP and classical potential choice.
• tasks/systems.md: bulk, liquids, surfaces, defects, interfaces, polymers, biomaterials.
• tasks/md_protocols.md: NVE/NVT/NPT protocols and ensembles.
• tasks/relaxation_static.md: geometry relaxation and static evaluation.
• tasks/trajectories.md: HDF5 trajectory reporting and export.
• tasks/analysis.md: trajectory and materials-property analysis.
• tasks/movies_visualization.md: MP4/GIF/XYZ/extxyz/OVITO/VMD visualization.
• tasks/validation_reproducibility.md: physical validation and reproducibility.
• tasks/batching_hpc.md: autobatching, large sweeps, GPU memory.
• tasks/troubleshooting.md: common errors.

Ready templates

Use files in templates/ as starting points:

• templates/config.yaml: canonical configuration schema.
• templates/run_md.py: production MD runner with trajectory reporting.
• templates/relax_static.py: relaxation and static precheck runner.
• templates/batch_sweep.py: high-throughput batched MD sweeps.
• templates/analyze_trajectory.py: energy/RDF/MSD/export analysis.
• templates/make_movie.py: HDF5 trajectory to XYZ/MP4/GIF.
• templates/validate_run.py: basic run-quality checks.

Utility scripts:

• scripts/torchsim_doctor.py: environment and GPU sanity check.
• scripts/trajectory_summary.py: inspect TorchSim HDF5 trajectory contents.

Important implementation cautions

• Many MLIP extras have incompatible dependency stacks, especially around e3nn. Use one clean environment per MLIP family when possible.
• Do not install every optional extra in one environment unless the user explicitly wants a compatibility matrix.
• MACE/fairchem/sevenn/mattersim/orb/nequip/nequix may require model downloads, CUDA-specific builds, or license/model-card review. State these assumptions.
• For publication-grade MD, do not treat a generic foundation model as validated for arbitrary chemistry. Include an explicit model-domain warning.
• For biological polymers/proteins, TorchSim atomistic MLIPs may not replace force-field MD in explicit solvent unless a suitable model and long-range electrostatics treatment are validated.
• For charged/ionic/polar systems, check whether the chosen model includes electrostatics/charge physics appropriate to the task.
• For high-temperature, fracture, chemical reaction, defect migration, surfaces, or interfaces, run small pilot simulations first and inspect structural stability before long production.

Note: Disable autobatcher on CPU pilot runs; TorchSim memory-estimation autobatching is intended for GPU memory management.