What's new
0.6.2 (October 2026)
- Uncertainty-driven exploration (HAL).
grace_uq halruns molecular dynamics biased towards high uncertainty and collects the structures where the extrapolation grade γ is high, as new training data. Step by step: the UQ workflows tutorial.
0.6.1 (3 September 2026)
- Foundation models re-exported. The distributed models were rebuilt to fix a NaN in
virial_sigma(the uncertainty contribution to the virial). Cached models now record where they were downloaded from, and the newgrace_models updatere-downloads only the copies that are out of date. - Tunable uncertainty threshold.
grace_uq build --threshold-percentile Qputs γ = 1 at the Q-th percentile of the training distribution instead of the default robust outlier fence, for training sets where many atoms read γ > 1.grace_uq inforeports which calibration was used. - Bulk prediction. A new
predict_structureshelper evaluates a whole list of structures with a single calculator instance;grace_predictandgrace_uq predictnow both use it.
0.6.0 (7 July 2026)
- Uncertainty estimates. Models can report a per-atom uncertainty score γ that flags extrapolation, for spotting unreliable predictions and for active learning. New command-line tool:
grace_uq. - LAMMPS Kokkos support. GRACE models can be exported for the fast Kokkos pair styles in LAMMPS, with the uncertainty score γ.
- Foundation models on HuggingFace. The released models include uncertainty support out of the box.
- GRACE-3L-OMAT/OAM. There are new, larger 3-layer models (
GRACE-3L-OMAT-largeandGRACE-3L-OMAT-large-ft-AM). - Faster and lighter by default. Foundation models use fp32 by default: about half the memory and twice the speed. The fp64 versions have the suffix
-fp64. - Better tooling.
grace_dashboardshows training curves and metrics in the browser;grace_modelslists, inspects and downloads models (including Kokkos weights); a fit can save and resume in the middle of an epoch.
Important Note
If a model was fitted with gracemaker version < 0.5.1, it will not be compatible with newer versions due to a format change.
You can convert it to the new format using the following command:
grace_utils -p seed/1/model.yaml -c seed/1/checkpoints/checkpoint.best_test_loss.index update_model
This will generate new files with the "-converted" suffix, which you can replace the old files (model.yaml and checkpoints) with.
GRACE - GRaph Atomic Cluster Expansion
Project GRACEmaker is a heavily modified and in large parts rewritten version of the PACEmaker software geared towards support for multi-component materials and graph architectures.
Documentation
Please see documentation for installation instructions and examples.
Tutorial
- Fitting tutorials and their materials: here.
- Uncertainty and active learning (CALM, HAL, selection for DFT): UQ workflows.
Also in a video format
Support
Also, you may join ACE support Zulip channel for additional resources: https://acesupport.zulipchat.com/join/xtwxu2grjbtg64m3vnhypi6p/
Reference
Please see
@article{lysogorskiy2026graph,
title={Graph atomic cluster expansion for foundational machine learning interatomic potentials},
author={Lysogorskiy, Yury and Bochkarev, Anton and Drautz, Ralf},
journal={npj Computational Materials},
volume={12},
number={1},
pages={114},
year={2026},
doi={10.1038/s41524-026-01979-1},
url={https://www.nature.com/articles/s41524-026-01979-1}
}
- For the CALM uncertainty indication (extrapolation grade
gamma): Y. Lysogorskiy, A. Bochkarev, R. Drautz, A latent-space extrapolation grade built into graph atomic cluster expansion foundation potentials. arXiv:2609.40060 (2026)
@article{lysogorskiy2026calm,
title={A latent-space extrapolation grade built into graph atomic cluster expansion foundation potentials},
author={Lysogorskiy, Yury and Bochkarev, Anton and Drautz, Ralf},
journal={arXiv preprint arXiv:2609.40060},
year={2026},
url={https://arxiv.org/abs/2609.40060}
}
@article{PhysRevX.14.021036,
title = {Graph Atomic Cluster Expansion for Semilocal Interactions beyond Equivariant Message Passing},
author = {Bochkarev, Anton and Lysogorskiy, Yury and Drautz, Ralf},
journal = {Phys. Rev. X},
volume = {14},
issue = {2},
pages = {021036},
numpages = {28},
year = {2024},
month = {Jun},
publisher = {American Physical Society},
doi = {10.1103/PhysRevX.14.021036},
url = {https://link.aps.org/doi/10.1103/PhysRevX.14.021036}
}
Metadata
Release files for tensorpotential 0.6.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| tensorpotential-0.6.2.tar.gz | 720.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| tensorpotential-0.6.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 1.3 MB
Release files / tensorpotential-0.6.2.tar.gz
| Download URL | tensorpotential-0.6.2.tar.gz |
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| Size | 720.0 kB |
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