openmaterials.ai / codes / MACE

MACE: equivariant many-body message passing force fields

A machine-learned interatomic potential whose messages carry four-body information, so two message-passing layers reach the accuracy that two-body models need five or six for.

What it is and how it works

Four-body messages in two layers

Each layer forms equivariant features of body order four by a symmetric tensor product on the nodes, not the edges. The receptive field stays at two cutoffs of 4 to 5 A, so the model parallelises across GPUs.

Architecture

Atomic cluster expansion features inside equivariant message passing: Bessel radial basis, spherical harmonics, Clebsch-Gordan coupling, linear readouts but the last.

Inputs and outputs

Positions and elements in, site energies out. Forces and stresses are analytic derivatives of the total energy with respect to positions and strain.

Speed on a GPU

Force latency of 10.5 ms (L=0) to 24.3 ms (L=2) per structure on an A100, 4 to 10 times faster than NequIP and BOTNet at matched accuracy (Batatia 2022).

MACE-MP-0 foundation model

Trained on MPtrj, about 1.5 million Materials Project configurations at the PBE level, it runs stable MD out of the box and fine-tunes on about 100 frames (Batatia 2025).

Interfaces

Trained models run through ASE, LAMMPS, OpenMM and JAX-MD. An additive DFT-D3 dispersion correction can be switched on for MACE-MP-0 (Kovács 2023, Batatia 2025).

What people do with it

COMP6 organic molecule benchmark

Trained on a coupled-cluster subset of ANI-1x, the 192-2 model reached 0.48 kcal/mol energy and 0.52 kcal/mol/A force MAE on COMP6, against 1.93 and 3.09 for ANI-1x. Kovács 2023

Liquid water from 1593 configurations

Fit to 1593 revPBE0-D3 water frames of 64 molecules, the model gave a density maximum near 270 K and a diffusion coefficient of 2.14e-9 m2/s from 200 ps NVT runs. Kovács 2023

Metal-organic frameworks, untrained

MACE-MP-0b3, trained on no MOF data, matched PBE energies of 13,912 QMOF structures to 0.040 eV/atom and placed CO2 at 2.27 A from Mg in Mg-MOF-74, the experimental distance. Batatia 2025

How it is used

  1. Collect reference data

    DFT energies, forces and stresses for your system, or start from the MACE-MP-0 weights.

  2. Train or fine-tune

    Fit a weighted Huber loss on energy, forces and stress. Fine-tune on about 100 frames.

  3. Load as a calculator

    The model becomes an ASE calculator or a LAMMPS, OpenMM or JAX-MD potential.

  4. Run and evaluate

    Energies in eV, forces in eV/A and stresses in eV/A^3 drive relaxation, MD and phonons.