Skip to content

Validate Configuration

Source code:

  • bff/workflows/configs.py
  • bff/workflows/validate.py
  • bff/workflows/runsims.py

Purpose

bff validate reruns selected parameter samples, usually drawn from the posterior learned by bff learn.

The campaign runtime keys intentionally match bff trainset as closely as possible.

Minimal Example

trainset_dir: ./validation-trainset
parameters: ../05-learning/posterior-samples.yaml
specs: ../02-training-data/trainset/specs.yaml
systems:
  - assets: ../01-prepare/colvars/ace-colvars/training/system-000
    n_steps: 1000
  - assets: ../01-prepare/colvars/ace-colvars/training/system-001
    n_steps: 1000
gmx_cmd: gmx
job_scheduler: local

Top-Level Keys

  • trainset_dir Output directory for the validation campaign.
  • parameters YAML file containing explicit parameter samples.
  • specs Force-field specification file used to reconstruct constrained parameters.
  • systems Non-empty list of prepared asset directories plus validation MD lengths.
  • gmx_cmd GROMACS executable.
  • job_scheduler Either local or slurm.
  • dispatch If true, launch jobs immediately after staging them.
  • compress If true, compress finished simulation outputs.
  • cleanup If true, remove temporary files after successful runs.
  • store Which trajectory outputs to keep. Defaults to ["xtc"].
  • slurm Optional Slurm runtime configuration.

systems[] Keys

  • assets Directory created by bff prepare, for example training/system-001.
  • n_steps Production MD length for this validation run.

Parameter File Format

Validation consumes YAML only. The expected structure is a mapping from explicit parameter name to a list of sampled values:

charge C1: [-0.5, -0.4, -0.3]
charge O1 O2: [-0.7, -0.6, -0.5]

The implicit charge is reconstructed from specs.yaml, so it does not need to appear in the file.