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SEAMM xnn Plug-in

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A SEAMM plug-in providing machine-learned force fields (MLFFs) trained with xnn as model chemistries, run as MDI engines.

Features

  • Advertises every xnn checkpoint found in the directories listed in xnn.ini to the Model Chemistry step as xnn:MLFF@<model>.

  • Launches xnn mdi – the checkpoint served as a resident MDI engine – for any step that drives a model chemistry over MDI: Energy (single points for many structures), LAMMPS (MD with the MLFF as the QM engine), Dimer Builder, Normal Mode Sampling.

  • Molecular and periodic systems (energy, forces and stress); runs on the CPU or a GPU (device in xnn.ini).

  • There is no xnn step to place in a flowchart: the models are used through Model Chemistry + Energy (or LAMMPS).

Note: xnn is published on PyPI under the name xnns; the import name and the xnn command are unchanged.

Acknowledgements

This package was created with the molssi-seamm/cookiecutter-seamm-plugin tool, which is based on the excellent Cookiecutter.

Developed by the Molecular Sciences Software Institute (MolSSI), which receives funding from the National Science Foundation under award CHE-2136142.

History

2026.9.26 – Internal: depend on seamm-manager rather than seamm-installer
  • The plug-in’s installer now builds on seamm-manager, which replaces seamm-installer for managing SEAMM installations. Nothing changes for users; this only lets the two packages stop being installed side by side.

2026.9.19.1 (2026-09-19)

  • Bugfix: the seamm-xnn environment file no longer installs PyTorch with conda. On Linux conda-forge resolves pytorch to a CPU-only build, so updating an environment that held a pip CUDA build of PyTorch silently lost the GPU and broke every compiled extension built against it, such as vesin-torch. PyTorch now comes from pip, which leaves a suitable existing installation alone.

  • Bugfix: pymdi now comes from conda-forge rather than pip. Only that build links the MDI library against MPI, which the -method MPI launch used for LAMMPS dynamics needs; with the PyPI build the engine stopped at “Error in MDI_Init: Failed to initialize MPI”.

  • Bugfix: xnn-step-installer reported the xnn version as “unknown” because it looked for a distribution named xnn, though the code is published as xnns.

  • Documented the two default model directories and their personal:/local: prefixes, the MPI launch path, and the effect of pointing xnn.ini at an environment shared with another code.

2026.9.19 (2026-09-19)

  • Model directories in xnn.ini may be given as personal:<subdir> (~/.seamm.d/data/Forcefields/<subdir>) or local:<subdir> (~/SEAMM/data/Forcefields/<subdir>), the same convention as the Forcefield step; the default is personal:xnn then local:xnn, a personal model shadowing a machine one of the same name. Each model’s source (e.g. personal:xnn/water.pt) is reported to the Model Chemistry step.

  • Installs xnn from PyPI as xnns; documents the Apple mps constraints.

2026.9.18 (2026-09-18)

  • Initial release. Provides xnn machine-learned force fields as xnn:MLFF@<model> model chemistries (checkpoints discovered from the directories in xnn.ini) and launches xnn mdi as the MDI engine for the Energy, LAMMPS and other MDI-driving steps. Includes xnn-step-installer for the seamm-xnn conda environment.

  • Plug-in created using the SEAMM plug-in cookiecutter.

Metadata

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