{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"},{"label":"Physics","url":"https://skillfed.io/packages/category/scientific-engineering-physics"},{"label":"Chemistry","url":"https://skillfed.io/packages/category/scientific-engineering-chemistry"}],"enrichment":{"capability":"CHGNet is a pretrained graph neural network that predicts energy, forces, stress, and magnetic moments for crystal structures, enabling structure optimization and molecular dynamics simulations with charge-informed accuracy.","skillfed_tags":["materials-science","machine-learning-potential","graph-neural-networks"],"use_cases":["Predict stability and properties of crystal structures for high-throughput materials screening without expensive DFT calculations.","Optimize atomic positions and cell parameters for new materials or hypothetical structures using CHGNet-based relaxation.","Run molecular dynamics simulations with charge-informed forces to study thermal properties and phase behavior.","Fine-tune the pretrained model on a custom dataset to improve accuracy for a specific class of materials or chemical system.","Calculate elastic properties (bulk modulus, shear modulus) and phonon properties using CHGNet with atomate2 workflows."],"what_it_does":"CHGNet is a pretrained universal neural network potential designed for atomistic modeling of crystal structures. It predicts energy, forces, stress, and magnetic moments by learning from over 1.5 million structures from the Materials Project, using charge information (inferred from DFT magnetic moments) to capture electron interactions and local ionic environments. The model achieves near-DFT accuracy while being orders of magnitude faster, making it suitable for high-throughput materials discovery and structure optimization.\n\nThe package integrates with pymatgen for structure handling and ase for molecular dynamics simulations. It provides multiple pretrained checkpoints (including versions trained on different datasets like MPtrj and R2SCAN) that can be loaded directly or fine-tuned for specific systems. Users can perform direct inference on static structures, run structure relaxations, or conduct charge-informed molecular dynamics without needing to train a model from scratch.","worth_installing":"Yes, with conditions. CHGNet is actively maintained, has no known vulnerabilities, and offers a practical alternative to DFT for structure prediction and relaxation. Install it if you need fast, charge-informed atomistic modeling and can manage the dependencies (torch, pymatgen, ase). Medium install friction is manageable given the prebuilt wheels. Not suitable if you require absolute DFT accuracy or work exclusively with systems outside the Materials Project's coverage."},"id":"chgnet","links":{"html":"https://skillfed.io/packages/chgnet","md":"https://skillfed.io/packages/chgnet.md","pypi":"https://pypi.org/project/chgnet/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-09-22","license_spdx":null,"license_treatment":"permissive","name":"chgnet","python_support":"supports_current","summary":"Pretrained Universal Neural Network Potential for Charge-informed Atomistic Modeling"},"popularity":{"monthly_downloads":143005,"position":11190,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.4.2"}