{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"}],"enrichment":{"capability":"MACE trains and evaluates machine learning interatomic potentials using equivariant message passing neural networks, designed for accurate molecular dynamics and materials simulations.","skillfed_tags":["molecular-dynamics","materials-science","equivariant-networks"],"use_cases":["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"],"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.\n\nThe 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.","worth_installing":"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."},"id":"mace-torch","links":{"html":"https://skillfed.io/packages/mace-torch","md":"https://skillfed.io/packages/mace-torch.md","pypi":"https://pypi.org/project/mace-torch/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-10","license_spdx":null,"license_treatment":"permissive","name":"mace-torch","python_support":"supports_current","summary":null},"popularity":{"monthly_downloads":128560,"position":11702,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.3.16"}