Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry

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Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry
  • Chen, C., Zuo, Y., Ye, W., Li, X., Deng, Z. & Ong, S. P. A critical review of machine learning of energy materials. Adv. Energy Mater. 10, 1903242 (2020).

    Google Scholar 

  • Schmidt, J., Marques, M. R. G., Botti, S. & Marques, M. A. L. Recent advances and applications of machine learning in solid-state materials science. npj Comput. Mater. 5, 83 (2019).

    Google Scholar 

  • Westermayr, J., Gastegger, M., Schütt, K. T. & Maurer, R. J. Perspective on integrating machine learning into computational chemistry and materials science. J. Chem. Phys. 154, 230903 (2021).

    PubMed 

    Google Scholar 

  • Oviedo, F., Ferres, J. L., Buonassisi, T. & Butler, K. T. Interpretable and explainable machine learning for materials science and chemistry. Acc. Mater. Res. 3, 597–607 (2022).

    Google Scholar 

  • Chen, C., Ye, W., Zuo, Y., Zheng, C. & Ong, S. P. Graph networks as a universal machine learning framework for molecules and crystals. Chem. Mater. 31, 3564–3572 (2019).

    Google Scholar 

  • Schmidt, J., Pettersson, L., Verdozzi, C., Botti, S. & Marques, M. A. L. Crystal graph attention networks for the prediction of stable materials. Sci. Adv. 7, eabi7948 (2021).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Gasteiger, J., Groß, J. & Günnemann, S. Directional message passing for molecular graphs. In International Conference on Learning Representations (ICLR, 2020).

  • Gasteiger, J., Giri, S., Margraf, J. T., Günnemann, S. Fast and uncertainty-aware directional message passing for non-equilibrium molecules. In 35th Conferenceon Neural Information Processing Systems. vol. 9 6790–6802 (NeurIPS, 2021).

  • Satorras, V. G., Hoogeboom, E., Welling, M. E(n) equivariant graph neural networks. In Proceedings of the 38th International Conference on Machine Learning. 9323–9332 (ACM, 2021).

  • Liu, Y. et al. Spherical message passing for 3D molecular graphs. In International Conference on Learning Representations. (ICLR, 2022).

  • Brandstetter, J., Hesselink, R., van der Pol, E., Bekkers, E. J., Welling, M. Geometric and Physical Quantities improve E(3) Equivariant Message Passing. In International Conference on Learning Representations (ICLR, 2022).

  • Kaba, S.-O. & Ravanbakhsh, S. Equivariant networks for crystal structures. In Advances in Neural Information Processing Systems (NeurIPS, 2022).

  • Yan, K., Liu, Y., Lin, Y. & Ji, S. Periodic graph transformers for crystal material property prediction. In Advances in Neural Information Processing Systems (NeurIPS, 2022).

  • Zhang, Y.-W. et al. Roadmap for the development of machine learning-based interatomic potentials. Modell. Simul. Mater. Sci. Eng. 33, 023301 (2025).

    Google Scholar 

  • Ko, T. W. & Ong, S. P. Recent advances and outstanding challenges for machine learning interatomic potentials. Nat. Comput. Sci. 3, 998–1000 (2023).

    PubMed 

    Google Scholar 

  • Unke, O. T. et al. Machine learning force fields. Chem. Rev. 121, 10142–10186 (2021).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A. & Müller, K.-R. SchNet – a deep learning architecture for molecules and materials. J. Chem. Phys. 148, 241722 (2018).

    PubMed 

    Google Scholar 

  • Schütt, K., Unke, O. & Gastegger, M. Equivariant message passing for the prediction of tensorial properties and molecular spectra. In Proceedings of the 38th International Conference on Machine Learning. 9377–9388 (2021).

  • Bartók, A. P., Payne, M. C., Kondor, R. & Csányi, G. Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons. Phys. Rev. Lett. 104, 136403 (2010).

    PubMed 

    Google Scholar 

  • Behler, J. & Parrinello, M. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys. Rev. Lett. 98, 146401 (2007).

    PubMed 

    Google Scholar 

  • Thompson, A. P., Swiler, L. P., Trott, C. R., Foiles, S. M. & Tucker, G. J. Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials. J. Comput. Phys. 285, 316–330 (2015).

    Google Scholar 

  • Drautz, R. Atomic cluster expansion for accurate and transferable interatomic potentials. Phys. Rev. B 99, 014104 (2019).

    Google Scholar 

  • Batzner, S. et al. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nat. Commun. 13, 2453 (2022).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Ko, T. W., Finkler, J. A., Goedecker, S. & Behler, J. Accurate fourth-generation machine learning potentials by electrostatic embedding. J. Chem. Theory Comput. 19, 3567–3579 (2023).

    PubMed 

    Google Scholar 

  • Kocer, E., Ko, T. W. & Behler, J. Neural network potentials: a concise overview of methods. Annu. Rev. Phys. Chem. 73, 163–186 (2022).

    PubMed 

    Google Scholar 

  • Ko, T. W., Finkler, J. A., Goedecker, S. & Behler, J. A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer. Nat. Commun. 12, 398 (2021).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Liao, Y.-L. & Smidt, T. Equiformer: equivariant graph attention transformer for 3D atomistic graphs. In International Conference on Learning Representations (ICLR) (ICLR, 2023).

  • Battaglia, P. W. et al. Relational inductive biases, deep learning, and graph networks. Preprint at (2018).

  • Chen, C. & Ong, S. P. A universal graph deep learning interatomic potential for the periodic table. Nat. Comput. Sci. 2, 718–728 (2022).

    PubMed 

    Google Scholar 

  • Chen, C., Zuo, Y., Ye, W., Li, X. & Ong, S. P. Learning properties of ordered and disordered materials from multi-fidelity data. Nat. Comput. Sci. 1, 46–53 (2021).

    PubMed 

    Google Scholar 

  • Ko, T. W. & Ong, S. P. Data-efficient construction of high-fidelity graph deep learning interatomic potentials. npj Comput. Mater. 11, 65 (2025).

    Google Scholar 

  • Han, J. et al. A survey of geometric graph neural networks: Data structures, models and applications. Front. Comput. Sci. 19, 1911375 (2025).

    Google Scholar 

  • Duval, A. et al. A hitchhiker’s guide to geometric gnns for 3d atomic systems. Preprint at (2023).

  • Batzner, S. et al. E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nat. Commun. 13, 2453 (2022).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Batatia, I., Kovacs, D. P., Simm, G., Ortner, C. & Csányi, G. MACE: Higher order equivariant message passing neural networks for fast and accurate force fields. Adv. Neural Inf. Process. Syst. 35, 11423–11436 (2022).

    Google Scholar 

  • Liao, Y.-L. & Smidt, T. Equiformer: equivariant graph attention transformer for 3d atomistic graphs. International Conference onLearning Representations (ICLR) (2023).

  • Wang, Y. et al. Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing. Nat. Commun. 15, 313 (2024).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Frank, J. T., Unke, O. T., Müller, K.-R. & Chmiela, S. A Euclidean transformer for fast and stable machine learned force fields. Nat. Commun. 15, 6539 (2024).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Gasteiger, J., Becker, F. & Günnemann, S. Gemnet: Universal directional graph neural networks for molecules. Adv. Neural Inf. Process. Syst. 34, 6790–6802 (2021).

    Google Scholar 

  • Fung, V., Zhang, J., Juarez, E. & Sumpter, B. G. Benchmarking graph neural networks for materials chemistry. npj Comput. Mater. 7, 1–8 (2021).

    Google Scholar 

  • Bandi, S., Jiang, C. & Marianetti, C. A. Benchmarking machine learning interatomic potentials via phonon anharmonicity. Mach. Learn. Sci. Technol. 5, 030502 (2024).

    Google Scholar 

  • Fu, X. et al. Forces are not enough: Benchmark and critical evaluation for machine learning force fields with molecular simulations. Trans. Mach. Learn. Res. (2023).

  • Deng, B. et al. CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling. Nat. Mach. Intell. 5, 1031–1041 (2023).

    Google Scholar 

  • Park, Y., Kim, J., Hwang, S. & Han, S. Scalable parallel algorithm for graph neural network interatomic potentials in molecular dynamics simulations. J. Chem. Theory Comput. 20, 4857–4868 (2024).

    PubMed 

    Google Scholar 

  • Batatia, I. et al. A foundation model for atomistic materials chemistry. Preprint at (2024).

  • Barroso-Luque, L. et al. Open materials 2024 (omat24) inorganic materials dataset and models. Preprint at (2024).

  • Neumann, M. et al. Orb: a fast, scalable neural network potential. Preprint at (2024).

  • Pelaez, R. P. et al. TorchMD-Net 2.0: fast neural network potentials for molecular simulations. J. Chem. Theory Comput. 20, 4076–4087 (2024).

    PubMed 

    Google Scholar 

  • Schütt, K. T., Hessmann, S. S. P., Gebauer, N. W. A., Lederer, J. & Gastegger, M. SchNetPack 2.0: a neural network toolbox for atomistic machine learning. J. Chem. Phys. 158, 144801 (2023).

    PubMed 

    Google Scholar 

  • Axelrod, S., Shakhnovich, E. & Gómez-Bombarelli, R. Excited state non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential. Nat. Commun. 13, 3440 (2022).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Fey, M. & Lenssen, J. E. Fast graph representation learning with PyTorch geometric. In Proc. ICLR 2019 Workshop on Representation Learning on Graphs and Manifolds (ICLR, 2019).

  • Abadi, M. et al. TensorFlow: a system for large-scale machine learning. In Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation. USA, p 265–283 (ACM, 2016).

  • Bradbury, J. et al. JAX: composable transformations of Python+NumPy programs. (2018).

  • Wang, M. Y. Deep graph library: Towards efficient and scalable deep learning on graphs. In ICLR workshop on representation learning on graphs and manifolds (2019).

  • Huang, X., Kim, J., Rees, B. & Lee, C.-H. Characterizing the efficiency of graph neural network frameworks with a magnifying glass. In 2022 IEEE International Symposium on Workload Characterization (IISWC). pp 160–170 (IEEE, 2022).

  • Ong, S. P. et al. Python Materials Genomics (pymatgen): a robust, open-source python library for materials analysis. Comput. Mater. Sci. 68, 314–319 (2013).

    Google Scholar 

  • Larsen, A. H. et al. The atomic simulation environment-a Python library for working with atoms. J. Phys. Condens. Matter 29, 273002 (2017).

    Google Scholar 

  • Simeon, G. & De Fabritiis, G. Tensornet: Cartesian tensor representations for efficient learning of molecular potentials. Adv. Neural Inf. Process. Syst. 36 (2024).

  • Vinyals, O., Bengio, S. & Kudlur, M. Order matters: Sequence to sequence for sets. In: Bengio, Y. & LeCun, Y. (eds.) Proc. 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2–4, 2016, Conference Track Proceedings (2016).

  • Glorot, X. & Bengio, Y. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. pp 249–256 (PMLR, 2010).

  • He, K., Zhang, X., Ren, S. & Sun, J. Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In 2015 IEEE International Conference on Computer Vision (ICCV). pp 1026–1034 (IEEE, 2015).

  • Bitzek, E., Koskinen, P., Gähler, F., Moseler, M. & Gumbsch, P. Structural relaxation made simple. Phys. Rev. Lett. 97, 170201 (2006).

    PubMed 

    Google Scholar 

  • Broyden, C. G., Dennis Jr, J. E. & Moré, J. J. On the local and superlinear convergence of quasi-Newton methods. IMA J. Appl. Math. 12, 223–245 (1973).

  • Liu, D. C. & Nocedal, J. On the limited memory BFGS method for large scale optimization. Math. Program. 45, 503–528 (1989).

    Google Scholar 

  • Garijo del Río, E., Mortensen, J. J. & Jacobsen, K. W. Local Bayesian optimizer for atomic structures. Phys. Rev. B 100, 104103 (2019).

    Google Scholar 

  • Berendsen, H. J. C., Postma, J. P. M., Van Gunsteren, W. F., DiNola, A. & Haak, J. R. Molecular dynamics with coupling to an external bath. J. Chem. Phys. 81, 3684–3690 (1984).

    Google Scholar 

  • Andersen, H. C. Molecular dynamics simulations at constant pressure and/or temperature. J. Chem. Phys. 72, 2384–2393 (1980).

    Google Scholar 

  • Schneider, T. & Stoll, E. Molecular-dynamics study of a three-dimensional one-component model for distortive phase transitions. Phys. Rev. B 17, 1302–1322 (1978).

    Google Scholar 

  • Nosé, S. A molecular dynamics method for simulations in the canonical ensemble. Mol. Phys. 52, 255–268 (1984).

    Google Scholar 

  • Hoover, W. G. Canonical dynamics: equilibrium phase-space distributions. Phys. Rev. A 31, 1695–1697 (1985).

    Google Scholar 

  • Liu, R. et al. MatCalc. (2024).

  • Sugita, Y. & Okamoto, Y. Replica-exchange molecular dynamics method for protein folding. Chem. Phys. Lett. 314, 141–151 (1999).

    Google Scholar 

  • Adams, D. Grand canonical ensemble Monte Carlo for a Lennard-Jones fluid. Mol. Phys. 29, 307–311 (1975).

    Google Scholar 

  • Dunn, A., Wang, Q., Ganose, A., Dopp, D. & Jain, A. Benchmarking materials property prediction methods: the Matbench test set and automatminer reference algorithm. npj Comput. Mater. 6, 1–10 (2020).

    Google Scholar 

  • Ramakrishnan, R., Dral, P. O., Rupp, M. & Von Lilienfeld, O. A. Quantum chemistry structures and properties of 134 kilo molecules. Sci. Data 1, 140022 (2014).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Wang, A. Y.-T., Kauwe, S. K., Murdock, R. J. & Sparks, T. D. Compositionally restricted attention-based network for materials property predictions. Npj Computational Mater. 7, 1–10 (2021).

    Google Scholar 

  • Pozdnyakov, S. N. & Ceriotti, M. Incompleteness of graph neural networks for points clouds in three dimensions. Mach. Learn.: Sci. Technol. 3, 045020 (2022).

    Google Scholar 

  • Smith, J. S., Nebgen, B., Lubbers, N., Isayev, O. & Roitberg, A. E. Less is more: sampling chemical space with active learning. J. Chem. Phys. 148, 241733 (2018).

    PubMed 

    Google Scholar 

  • Kaplan, A. D. et al. A foundational potential energy surface dataset for materials. Preprint at (2025).

  • Zhang, S. Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential. Nat. Chem. 16, 727–734 (2024).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Kovács, D. P. et al. Mace-off: Shortrangetransferable machine learning force fields for organic molecules. J. Am. Chem. Soc. 147, 17598–17611 (2025).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Qi, J., Ko, T. W., Wood, B. C., Pham, T. A. & Ong, S. P. Robust training of machine learning interatomic potentials with dimensionality reduction and stratified sampling. npj Comput. Mater. 10, 43 (2024).

    Google Scholar 

  • Gonzales, C., Fuemmeler, E., Tadmor, E. B., Martiniani, S., Miret, S. Benchmarking of universal machine learning interatomic potentials for structural relaxation. In AI for Accelerated Materials Design (NeurIPS, 2024).

  • Yu, H., Giantomassi, M., Materzanini, G., Wang, J. & Rignanese, G.-M. Systematic assessment of various universal machine-learning interatomic potentials. Mater. Genome Eng. Adv. 2, e58 (2024).

    Google Scholar 

  • Pan, H. Benchmarking coordination number prediction algorithms on inorganic crystal structures. Inorg. Chem. 60, 1590–1603 (2021).

    PubMed 

    Google Scholar 

  • Loew, A., Sun, D., Wang, H.-C., Botti, S. & Marques, M. A. Universal machine learning interatomic potentials are ready for phonons. npj Comput Mater 11, 178 (2025).

    Google Scholar 

  • Batatia, I. et al. A foundation model for atomistic materials chemistry. Preprint at (2023).

  • Zuo, Y. Performance and cost assessment of machine learning interatomic potentials. J. Phys. Chem. A 124, 731–745 (2020).

    PubMed 

    Google Scholar 

  • Zhao, J. et al. Complex Ga2O3 polymorphs explored by accurate and general-purpose machine-learning interatomic potentials. npj Comput. Mater. 9, 159 (2023).

    Google Scholar 

  • Chen, Z., Du, T., Krishnan, N. A., Yue, Y. & Smedskjaer, M. M. Disorder-induced enhancement of lithium-ion transport in solid-state electrolytes. Nat. Commun. 16, 1057 (2025).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Grimme, S., Antony, J., Ehrlich, S. & Krieg, H. A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. J. Chem. Phys. 132, 154104 (2010).

    PubMed 

    Google Scholar 

  • Poltavsky, I. et al. Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023. Chem. Sci. 16, 3720–3737 (2025).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Poltavsky, I. et al. Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023. Chem. Sci. 16, 3738–3754 (2025).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Bihani, V. et al. EGraFFBench: evaluation of equivariant graph neural network force fields for atomistic simulations. Digit. Discov. 3, 759–768 (2024).

    Google Scholar 

  • Chen, C. et al. Accelerating computational materials discovery with machine learning and cloud high-performance computing: from large-scale screening to experimental validation. J. Am. Chem. Soc. 146, 20009–20018 (2024).

    PubMed 

    Google Scholar 

  • Ojih, J., Al-Fahdi, M., Yao, Y., Hu, J. & Hu, M. Graph theory and graph neural network assisted high-throughput crystal structure prediction and screening for energy conversion and storage. J. Mater. Chem. A 12, 8502–8515 (2024).

    Google Scholar 

  • Sivak, J. T. et al. Discovering high-entropy oxides with a machine-learning interatomic potential. Phys. Rev. Lett. 134, 216101 (2025).

    PubMed 

    Google Scholar 

  • Taniguchi, T. Exploration of elastic moduli of molecular crystals via database screening by pretrained neural network potential. CrystEngComm 26, 631–638 (2024).

    Google Scholar 

  • Mathew, K. et al. Atomate: a high-level interface to generate, execute, and analyze computational materials science workflows. Computational Mater. Sci. 139, 140–152 (2017).

    Google Scholar 

  • Miret, S., Lee, K. L. K., Gonzales, C., Nassar, M. & Spellings, M. The open MatSci ML toolkit: a flexible framework for machine learning in materials science. Trans. Mach. Learn. Res. (2023).

  • te Velde, G. et al. Chemistry with ADF. J. Comput. Chem. 22, 931–967 (2001).

    Google Scholar 

  • Schwalbe-Koda, D., Hamel, S., Sadigh, B., Zhou, F. & Lordi, V. Model-free estimation of completeness, uncertainties, and outliers in atomistic machine learning using information theory. Nat. Commun. 16, 4014 (2025).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Musielewicz, J., Lan, J., Uyttendaele, M. & Kitchin, J. R. Improved uncertainty estimation of graph neural network potentials using engineered latent space distances. J. Phys. Chem. C. 128, 20799–20810 (2024).

    Google Scholar 

  • Podryabinkin, E. V. & Shapeev, A. V. Active learning of linearly parametrized interatomic potentials. Comput. Mater. Sci. 140, 171–180 (2017).

    Google Scholar 

  • Kulichenko, M. et al. Uncertainty-driven dynamics for active learning of interatomic potentials. Nat. Comput. Sci. 3, 230–239 (2023).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Park, H., Onwuli, A., Butler, K. T. & Walsh, A. Mapping inorganic crystal chemical space. Faraday Discuss. 256, 601–613 (2025).

    PubMed 

    Google Scholar 

  • Onwuli, A., Hegde, A. V., Nguyen, K. V., Butler, K. T. & Walsh, A. Element similarity in high-dimensional materials representations. Digital Discov. 2, 1558–1564 (2023).

    Google Scholar 

  • Landrum, G. et al. RDKit: a software suite for cheminformatics, computational chemistry, and predictive modeling. Greg. Landrum 8, 5281 (2013).

    Google Scholar 

  • RRuddigkeit, L., Van Deursen, R., Blum, L. C. & Reymond, J.-L. Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17. J. Chem. Inf. Model. 52, 2864–2875 (2012).

    Google Scholar 

  • Tran, R. Surface energies of elemental crystals. Sci. Data 3, 1–13 (2016).

    Google Scholar 

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