ACEsuit/mace

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

1324 stars · 469 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

MACE is a Python library implementing fast and accurate machine learning interatomic potentials using higher-order equivariant message passing neural networks. It includes training and evaluation tooling plus pretrained foundation models (MACE-MP, MACE-OFF, MACE-Polar) usable with ASE.

Use cases

• train machine learning interatomic potentials for molecular simulations
• run molecular dynamics with a neural network force field
• predict energies and forces for atomic structures
• simulate materials with a pretrained universal force field
• model organic molecules with transferable force fields
• finetune a foundation model on my own DFT data

When to choose

• you need accurate ML interatomic potentials with equivariant message passing
• you want pretrained universal force fields for materials or organic molecules
• you work with ASE and want GPU-accelerated potential evaluation

When to avoid

• you need classical fixed-form force fields rather than learned potentials
• you need a non-Python or JAX-first training workflow (see mace-jax instead)
• your project requires a permissively documented stable API - documentation is partial

Facets

library · maturity active

machine-learning deep-learning simulation gpu-computing machine-learning chemistry simulation python interatomic-potentials molecular-dynamics equivariant-neural-networks force-fields graph-neural-networks computational-chemistry materials-science linux macos gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="ACEsuit/mace")