Materials Discovery

CHGNet is a pretrained, charge-informed neural network potential for crystal structures, predicting energies, forces, stresses and magnetic moments for relaxation and molecular dynamics workflows.

Computational Chemistry · Materials Discovery

MEGNet is a deprecated TensorFlow graph-network implementation with pretrained molecular and crystal property models, training utilities, and transferable elemental embeddings.

Molecular Property Prediction · Materials Discovery

MODNet is a Python framework for supervised materials-property prediction from composition or crystal structure, with joint learning for multiple targets and two documented pretrained models.

Materials Discovery

matminer is a Python library for materials-science data mining, bringing together community datasets, data retrieval methods and featurizers with citation support for research workflows.

Materials Discovery

Pymatgen is a Python materials-analysis library combining structure and file-format support with phase diagrams, electronic-structure analysis, thermodynamic calculations and database integrations.

Materials Discovery

Materials Project API is the Python client project published as mp-api, with official data-access documentation and an optional MCP server entry point declared in its package configuration.

Materials Discovery · Scientific Data

Catalysis-oriented atomistic modeling workflows in fairchem, using pretrained UMA models and ASE calculators for surface relaxation, energy prediction and molecular dynamics.

Materials Discovery

Matbench Discovery benchmarks machine-learning models for crystal stability and atomistic simulation tasks, using an interactive leaderboard to compare accuracy, robustness, and computational cost.

Materials Discovery

Matbench provides 13 curated materials-science machine learning tasks for benchmarking property-prediction methods, with benchmark data, leaderboards and a pip-installable package.

Materials Discovery

DeePMD-kit is a toolkit for training and fine-tuning Deep Potential interatomic models from quantum-mechanical reference data, then exporting them for inference and molecular dynamics.

Computational Chemistry · Materials Discovery

Allegro implements an E(3)-equivariant interatomic potential as a NequIP extension, with documented GPU acceleration options and a separate plugin for LAMMPS simulations.

Computational Chemistry · Materials Discovery

NequIP is an open-source framework for building E(3)-equivariant interatomic potentials, with training, pre-trained foundation potentials, and integration with ASE and LAMMPS.

Computational Chemistry · Materials Discovery

MACE provides higher-order equivariant machine-learning interatomic potentials, with workflows for training on atomistic data, evaluating configurations, and using or fine-tuning pretrained models.

Computational Chemistry · Materials Discovery