---
id: mace-torch
version: "0.3.16"
license: unclear
license_treatment: permissive
maintenance: active
---

# mace-torch

License: permissive · Maintenance: active · Downloads: 128.6K/mo

## What it is and what it does

MACE is a PyTorch-based framework for training machine learning interatomic potentials using higher-order equivariant message passing. It combines deep learning with physical symmetry constraints to predict atomic forces and energies for molecular dynamics simulations and materials discovery. The package provides command-line tools for training on atomic configurations, evaluating model accuracy, and finetuning pretrained foundation models. It integrates with ASE (Atomic Simulation Environment) for seamless use in molecular dynamics workflows and supports multi-GPU distributed training for large datasets.

The framework handles heterogeneous training data (bulk structures with stress, isolated molecules, etc.) and offers flexible model architecture control through equivariant irreps. It includes utilities for experiment tracking, caching, and visualization of training progress. The package depends on PyTorch, e3nn for equivariant operations, and several scientific libraries (numpy, h5py, pandas, matplotlib) for data handling and analysis.

Use it for:

- Train custom interatomic potentials on your own atomic structure datasets for materials-specific simulations
- Finetune pretrained MACE foundation models (MACE-MP, MACE-OFF) on specialized datasets to improve accuracy for your domain
- Run molecular dynamics simulations in ASE using MACE models as the force field calculator
- Evaluate model accuracy on test sets and compare different equivariant architectures for your materials system
- Perform distributed training on large datasets across multiple GPUs using PyTorch's DistributedDataParallel

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

MACE trains and evaluates machine learning interatomic potentials using equivariant message passing neural networks, designed for accurate molecular dynamics and materials simulations.

Yes. MACE is actively maintained, has low install friction, carries no known vulnerabilities, and is permissively licensed. Install it if you need to train or deploy machine learning interatomic potentials for molecular dynamics or materials simulations. Be aware that PyTorch 2.4.1 is not supported and float64 training requires PyTorch 2.2+; verify your PyTorch version before use.

## Install

pip install mace-torch
uv add mace-torch
poetry add mace-torch

## Installing mace-torch

Before you install: Low install friction with a pure Python wheel. Active maintenance with recent commits and steady updates; requires PyTorch 1.12+ and Python 3.9+, with a note that PyTorch 2.4.1 is not supported and float64 training requires PyTorch 2.2 or later.

License in practice: Licensed permissively, allowing commercial and private use without restriction.

Quickstart:

pip install mace-torch

import torch
from mace.calculators import MACECalculator

# Load a pretrained model or train your own
calc = MACECalculator(model_paths="path/to/model.model", device="cpu")

PyTorch >= 1.12 must be installed separately; PyTorch 2.4.1 is not supported; float64 training requires PyTorch 2.2 or later; Python >= 3.9 required

Verify before relying:

- Whether pretrained foundation models (MACE-MP, MACE-OFF, MACE-Polar) are included in the package or must be downloaded separately
- GPU memory requirements for training models of different sizes
- Performance comparison with other interatomic potential frameworks

## Package facts

- License: not declared (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 128.6K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

machine learning interatomic potentials, equivariant neural networks molecules, molecular dynamics force fields, materials simulation neural networks, atomic structure prediction, graph neural networks chemistry, equivariant message passing, molecular-dynamics, materials-science, equivariant-networks

[View on SkillFed](https://skillfed.io/packages/mace-torch) · [View on PyPI](https://pypi.org/project/mace-torch/)