{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/10"}],"enrichment":{"capability":"Provides pretrained machine learning models for predicting molecular and materials properties, integrated with ASE for structure relaxation, molecular dynamics, and quantum chemistry calculations.","skillfed_tags":["materials-science","molecular-dynamics","gpu-accelerated"],"use_cases":["Relax adsorbate-surface systems for heterogeneous catalysis research without running expensive DFT calculations.","Perform molecular dynamics simulations of molecules and polymers at scale using GPU acceleration.","Predict formation energies and structural stability of inorganic crystals for materials discovery workflows.","Calculate spin gaps and other quantum properties of molecules for computational chemistry studies.","Run large-scale MD simulations (100k+ atoms) on multi-GPU clusters for industrial materials modeling.","Estimate properties of metal-organic frameworks and molecular crystals for screening applications."],"what_it_does":"Fairchem-core is FAIR Chemistry's centralized repository of machine learning models for materials science and quantum chemistry. It provides pretrained models (UMA series) that can predict molecular and materials properties across multiple domains: catalysis, oxides, molecules, polymers, inorganic crystals, MOFs, and molecular crystals. The package integrates with ASE (Atomic Simulation Environment) via the FAIRChemCalculator, allowing users to perform structure relaxations, molecular dynamics simulations, and property calculations by selecting a task name appropriate to their domain.\n\nThe package depends on a large scientific stack including torch, ray, scipy, and specialized libraries like e3nn and hydra-core. It supports multi-GPU and multi-node inference for large-scale simulations. Version 2 is a breaking change from version 1 and incompatible with older pretrained models. The latest UMA-1.2 release (March 2026) claims approximately 50% faster inference and expanded data coverage for catalysts, molecules, and polymers.","worth_installing":"Yes, if you work in computational chemistry or materials science and want fast, pretrained ML-based property predictions integrated with ASE. Requires GPU access, a Hugging Face account, and familiarity with the ASE ecosystem. Active maintenance, permissive license, and no known vulnerabilities support adoption. Version 2 is a breaking change\u2014verify compatibility with your existing workflows before upgrading from version 1."},"id":"fairchem-core","links":{"html":"https://skillfed.io/packages/fairchem-core","md":"https://skillfed.io/packages/fairchem-core.md","pypi":"https://pypi.org/project/fairchem-core/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-08","license_spdx":null,"license_treatment":"permissive","name":"fairchem-core","python_support":"supports_current","summary":"Machine learning models for chemistry and materials science by the FAIR Chemistry team"},"popularity":{"monthly_downloads":110269,"position":12474,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.21.0"}