Changelog

All notable changes to metatrain are documented here, following the keep a changelog format. This project follows Semantic Versioning.

Unreleased

Added

• A new dataset format, MemmapDataset, allows storing data on disk in a memory-mapped format, improving performance compared to DiskDataset on some filesystems.

• FlashMD was added as a new architecture allowing long-stride molecular dynamics simulations. Its implementation is based on PET.

Changed

• PET model received a major update, including new default hyperparameters, a new transformer architecture, and a new featurizer. Please refer to the updated documentation for more details.

• The SOAP-BPNN and PET trainers now uses a cosine annealing learning rate scheduler with warmup.

• NanoPET has been deprecated in favor of the stable PET architecture. The deprecated.nanopet architecture is still available for loading old checkpoints, but it will not receive any updates or bug fixes.

• The NanoPET and GAP architectures now use the new composition model, and the old composition model has been removed.

• The LLPR module is now a stable architecture, instead of a utility module. It can be trained from the command line in the same way as other architectures.

• We now require Python >= 3.10

• The Scaler model in metatrain now calculates per-block and per-property scales. For atomic targets, it calculates per-element scales.

Version 2025.10 - 2025-09-09

Fixed

• Fixed a bug with the composition model during transfer-learning

Changed

• Refactored the loss.py module to provide an easier to extend interface for custom loss functions.

• Updated the trainer checkpoints to account for changes in the loss-related hypers.

Version 2025.9.1 - 2025-08-21

Fixed

• Fixed incompatibilities with PET-MAD when updating checkpoints and exporting

Version 2025.9 - 2025-08-18

Added

• Use the best model instead of the latest model for evaluation at the end of training.

• Log the best epoch when loading checkpoints.

• Allow changing the scheduler factor in PET.

• Introduce checkpoint versioning and updating.

• Added CI tests on GPU.

• Log the number of model parameters before training starts.

• Add additional logs to the checkpoints, model, and output directories at the end of training.

• Cache files locally and re-use them when downloading checkpoints and models from Hugging Face.

• extra_data is now a valid section in the options.yaml file, allowing users to add custom data to the training set. The data is included in the dataloader and can be used in custom loss functions or models.

• mtt eval can now evaluate models on a DiskDataset.

Changed

• Updated to a new general composition model.

• Updated to a new implementation of LLPR.

Fixed

• Fixed device and dtype not being set during LoRA fine-tuning in PET.

• Log messages are now shown when training with restart="auto".

• Fixed incorrect sub-section naming in the Wandb logger.

Version 2025.8 - 2025-06-11

Changed

• Checkpoints for fine-tuning files are now passed from the options.yaml.

Version 2025.7 - 2025-05-27

Changed

• Metatrain is now built on top of metatomic instead of metatensor.torch.atomistic. Please refer to https://docs.metatensor.org/metatomic/ to find how to use the new models.

Version 2025.6 - 2025-04-28

Fixed

• PET can now evaluate on single-atom structures without crashing

• The metatrain dataloader doesn’t load all batches ahead of each epoch anymore

Added

• NanoPET and PET can now train on non-conservative stresses

• Users can now choose the name of the extension directory in mtt train and mtt export via the --extensions (or -e) option

• Update to metatensor-torch-0.7.6, adding support for torch 2.7

• PET now supports gradient clipping as a new training hyperparameter

Changed

• Training and exporting models without extensions will no longer lead to the creation of an empty directory for the extensions

• The SOAP-BPNN model now uses torch-spex instead of featomic as its SOAP backend

• PET from the previous version is now deprecated and accessible as deprecated.pet, while the old NativePET (experimental.nativepet) is now called PET (pet from training option files)

• The Angstrom character is now represented as A and not Å in the training logs

Version 2025.5 - 2025-04-13

Fixed

• Fix more composition model issues

Added

• Update to metatensor-torch-0.7.5 to allow training on non_conservative_forces and non_conservative_stress targets

• Add NativePET as a readable, efficient, backward-compatible PET implementation

• Added Wandb logger

• Save loss history in a .csv file

Version 2025.4 - 2025-03-29

Changed

• upgraded to metatensor.torch 0.7.4, which gives access to batched ASE evaluation

Version 2025.3 - 2025-03-25

Fixed

• Fixed a bug in the composition model, affecting SOAP-BPNN and nanoPET

Changed

• metatrain.util.io.load_model() does not copy a remote model to the current directory.

Version 2025.2 - 2025-03-11

Added

• Implement a long-range featurizer as a utility for all models

• Speed up system preparation

Changed

• Remove biases in SOAP-BPNN’s linear layers

Fixed

• Fix NanoPET multi-GPU error message

• Fix device for fixed composition weights

Version 2025.1 - 2025-02-20

Added

• Support for Python 3.13 and ase >= 3.23

Fixed

• Some irrelevant autograd warnings

Version 2025.0 - 2025-02-19

Added

• First release outside of the lab