ACEsuit

weights

open(ungated foundation checkpoints published as GitHub release assets)

data

open(MPTrj, sAlex, OMat, MATPES, and OMol training datasets publicly released)

code

open(full data preprocessing, distributed training, and fine-tuning pipeline)

license

MIT(code and the MACE-MP-0a, MACE-MP-0b3, and MACE-MPA-0 checkpoints)+ASL(MACE-OMAT-0, MACE-MATPES-PBE-0, MACE-MATPES-r2SCAN-0, MACE-OMOL-0, MACE-OFF23, and MACE-MH-0/1 checkpoints

MACE is otherwise a complete release: open corpus, open pretraining pipeline, open weights. Some checkpoints ship under MIT, but the newer foundation checkpoints ship under the Academic Software License, which states that it is not an open-source license because it forbids commercial use; where a release mixes licenses like this, the more restrictive one governs the score, so the release counts as restricted.

• https://raw.githubusercontent.com/ACEsuit/mace/main/LICENSE.md recorded 2026-09-10

  MIT License body with an added ACEsuit/mace copyright line, read directly because the license endpoint reports NOASSERTION; it covers the code, not the checkpoints.

• https://raw.githubusercontent.com/ACEsuit/mace/main/README.md recorded 2026-09-10

  Tabulates the recommended foundation models with a download link, a named training dataset, and a licence column per checkpoint — MIT for MACE-MP-0a, 0b3 and MPA-0, ASL for OMAT-0, MATPES and OMOL-0 and MH — and documents mace_run_train, preprocess_data.py, multi-GPU training, and foundation-model finetuning.

• https://raw.githubusercontent.com/gabor1/ASL/main/ASL.md recorded 2026-09-10

  States the Academic Software Licence is not an open-source licence because it does not allow commercial use, and grants rights only for academic non-commercial use.

Adoption is measured from PyPI downloads of mace-torch, the declared package that installs the foundation checkpoints, rather than from GitHub stars. Four first-party Hugging Face repositories under mace-foundations also exist but report zero downloads, so PyPI remains the signal.

• https://api.github.com/repos/ACEsuit/mace recorded 2026-09-10

  stargazers_count: 1345; archived: false; fork: false — recorded so the superseded stars signal stays visible.

• https://pypistats.org/api/packages/mace-torch/recent recorded 2026-10-07

  76,361 downloads in the trailing 30 days for mace-torch (pypistats last_month)

Most of the family is one checkpoint per domain, with small, medium and large as sizes rather than scales. MACE-MH is credited as a second attribute: a single checkpoint whose card claims unified cross-domain capability across inorganic crystals, molecular systems, surface chemistry and reactive organic chemistry, with separate heads for OMAT PBE, OMOL, OC20, SPICE, RGD1, MPTraj and MatPES.

• https://raw.githubusercontent.com/ACEsuit/mace/main/README.md recorded 2026-09-10

  Documents finetuning a foundation model with mace_run_train --foundation_model, states the pretrained models can be used directly for inference or as a starting point for fine-tuning, and describes outputs as energies, forces and stresses.