History#

2026.10.14.1 – The Active Learning step for flowcharts

  • The Active Learning step now holds four sub-steps: Train Committee, Check Committee, Score Pool and Select Frames, which do in a flowchart what the seamm-al command does. Each can use the committee, scores and thresholds made earlier in the job, and their dialogs show only the settings that apply.

  • Training, checking and scoring run in the MLFF environment of the xnn plug-in (xnn.ini) as tasks of the job, with nothing to install there; selecting runs in SEAMM.

  • Score Pool takes candidates from the job’s database, with the standard selection of systems and configurations, or from extxyz and SDF files. Select Frames puts selected configurations from the database in a variable for later steps and can copy them into a new system.

  • Train Committee can save the committee as an xnn model, which the Model Chemistry step then offers (with xnn-step 2026.10.14 or later) for molecular dynamics with the committee mean, recording the members’ spreads; with lammps-step 2026.10.14.1 or later this also runs on the CPU.

  • Candidate files may be gzipped, such as the trajectories LAMMPS writes.

  • Requires xnn-step 2026.10.14 or later.

2026.10.14 – Initial release: query-by-committee active learning for MLFFs

  • A committee of machine-learned force fields, trained on different folds of the data, disagrees where the model is likely wrong; the seamm-al command uses that disagreement to choose what to label next. This first release provides the library and the command line; the SEAMM flowchart step is a placeholder.

  • seamm-al score: the committee’s spreads (energy, forces, the largest per-atom force spread, atomic and molecular pressure) for every frame of extxyz or SEAMM SDF pools, with the total charge passed to the models. Molecules follow SEAMM’s own rule (molsystem’s bond perception, ions on their own), applied without SEAMM in MLFF environments, or a SEAMM structure’s own bonds.

  • seamm-al select: rank the frames, or keep those past thresholds, and take them within a core-hour budget, with costs per kind or group, caps per column and exclusions; or draw at random (optionally stratified) before any committee exists. The chosen frames are copied byte for byte from the input files.

  • seamm-al train: train the k members of a committee from an xnn recipe, with stratified folds, a fixed share of cell batches and a manifest of the folds, seeds, data and code versions. Requires xnn 0.13 or later.

  • seamm-al check: whether the spreads predict the out-of-fold errors (Spearman correlations, threshold calibration) and the health of each member (losses, errors, leave-one-out spreads), with an option to drop a member.

  • seamm-al engine: the committee mean as one MDI engine for LAMMPS, writing the spreads every N steps and flagging frames past a threshold.

  • seamm-al data: check a LAMMPS data file for what atom_style atomic can read.