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Bayesic Force Fields

Bayesic Force Fields (BFF) is a workflow-oriented toolkit for learning fixed-charge molecular force fields from trajectory-derived observables.

Associated publication: Bayesian Learning for Accurate and Robust Biomolecular Force Fields

Preprint: arXiv:2511.05398

For exact reproduction of the published paper data, use the archived Git tag v0.0.1. The current bfflearn release line documents and ships the post-paper refactored workflow.

The public interface is intentionally small:

  • bff prepare stages equilibrated systems and reusable training assets.
  • bff reference executes staged CP2K reference calculations.
  • bff trainset generates a sampled trainset from prepared assets.
  • bff qoi computes quantities of interest from trainset and reference data.
  • bff train fits surrogate models from analyzed QoI datasets.
  • bff learn performs posterior inference from trained surrogate models.
  • bff validate reruns selected posterior samples with the same campaign machinery used for training.

Design Goals

  • readable YAML configs with one clear job per file
  • minimal hidden behavior between workflow stages
  • reusable prepared training assets
  • custom QoI routines that are easy to write
  • packaging and release metadata clean enough for public deployment

Where To Start