Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry
Abstract Graph deep learning models, which incorporate a natural inductive bias for atomic structures, are of immense interest in materials science and chemistry. Here, we introduce the Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry. B...
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01742-y |
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| _version_ | 1849331834628866048 |
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| author | Tsz Wai Ko Bowen Deng Marcel Nassar Luis Barroso-Luque Runze Liu Ji Qi Atul C. Thakur Adesh Rohan Mishra Elliott Liu Gerbrand Ceder Santiago Miret Shyue Ping Ong |
| author_facet | Tsz Wai Ko Bowen Deng Marcel Nassar Luis Barroso-Luque Runze Liu Ji Qi Atul C. Thakur Adesh Rohan Mishra Elliott Liu Gerbrand Ceder Santiago Miret Shyue Ping Ong |
| author_sort | Tsz Wai Ko |
| collection | DOAJ |
| description | Abstract Graph deep learning models, which incorporate a natural inductive bias for atomic structures, are of immense interest in materials science and chemistry. Here, we introduce the Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry. Built on top of the popular Deep Graph Library (DGL) and Python Materials Genomics (Pymatgen) packages, MatGL is designed to be an extensible “batteries-included” library for developing advanced model architectures for materials property predictions and interatomic potentials. At present, MatGL has efficient implementations for both invariant and equivariant graph deep learning models, including the Materials 3-body Graph Network (M3GNet), MatErials Graph Network (MEGNet), Crystal Hamiltonian Graph Network (CHGNet), TensorNet and SO3Net architectures. MatGL also provides several pre-trained foundation potentials (FPs) with coverage of the entire periodic table, and property prediction models for out-of-box usage, benchmarking and fine-tuning. Finally, MatGL integrates with PyTorch Lightning to enable efficient model training. |
| format | Article |
| id | doaj-art-bb22674daaff4ea691d10638ef28cb56 |
| institution | Kabale University |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-bb22674daaff4ea691d10638ef28cb562025-08-20T03:46:23ZengNature Portfolionpj Computational Materials2057-39602025-08-0111111410.1038/s41524-025-01742-yMaterials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistryTsz Wai Ko0Bowen Deng1Marcel Nassar2Luis Barroso-Luque3Runze Liu4Ji Qi5Atul C. Thakur6Adesh Rohan Mishra7Elliott Liu8Gerbrand Ceder9Santiago Miret10Shyue Ping Ong11Aiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California San DiegoDepartment of Materials Science and Engineering, University of California BerkeleyIntel LabsDepartment of Materials Science and Engineering, University of California BerkeleyAiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California San DiegoAiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California San DiegoAiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California San DiegoAiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California San DiegoAiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California San DiegoDepartment of Materials Science and Engineering, University of California BerkeleyIntel LabsAiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California San DiegoAbstract Graph deep learning models, which incorporate a natural inductive bias for atomic structures, are of immense interest in materials science and chemistry. Here, we introduce the Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry. Built on top of the popular Deep Graph Library (DGL) and Python Materials Genomics (Pymatgen) packages, MatGL is designed to be an extensible “batteries-included” library for developing advanced model architectures for materials property predictions and interatomic potentials. At present, MatGL has efficient implementations for both invariant and equivariant graph deep learning models, including the Materials 3-body Graph Network (M3GNet), MatErials Graph Network (MEGNet), Crystal Hamiltonian Graph Network (CHGNet), TensorNet and SO3Net architectures. MatGL also provides several pre-trained foundation potentials (FPs) with coverage of the entire periodic table, and property prediction models for out-of-box usage, benchmarking and fine-tuning. Finally, MatGL integrates with PyTorch Lightning to enable efficient model training.https://doi.org/10.1038/s41524-025-01742-y |
| spellingShingle | Tsz Wai Ko Bowen Deng Marcel Nassar Luis Barroso-Luque Runze Liu Ji Qi Atul C. Thakur Adesh Rohan Mishra Elliott Liu Gerbrand Ceder Santiago Miret Shyue Ping Ong Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry npj Computational Materials |
| title | Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry |
| title_full | Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry |
| title_fullStr | Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry |
| title_full_unstemmed | Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry |
| title_short | Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry |
| title_sort | materials graph library matgl an open source graph deep learning library for materials science and chemistry |
| url | https://doi.org/10.1038/s41524-025-01742-y |
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