MACE Backbone

MACE is a series of fast and accurate machine learning interatomic potentials with higher order equivariant message passing developed by Ilyes Batatia, Gregor Simm, David Kovacs, and the group of Gabor Csanyi in University of Cambridge. The MACE series released its first foundation model, MACE-MP-0, in 2023, making it one of the earliest foundation models in the materials domain. To date, MACE has spawned several versions of its foundation models (see MACE versions for details) and has earned top marks on numerous leaderboards.

Installation

MACE can be directly installed with pip:

pip install --upgrade pip
pip install mace-torch

or it can be installed from source code:

git clone https://github.com/ACEsuit/mace.git
pip install ./mace

Key Features

MACE adopts an equivariant neural-network paradigm and delivers energy-conserving predictions of forces and stresses. For details on the specific features of each MACE version, please consult the introduction here: MACE versions

License

The MACE backbone is available under MIT License