A framework to evaluate machine learning crystal stability predictions
Bergerhoff, G., Hundt, R., Sievers, R. & Brown, I. D. The inorganic crystal structure data base. J. Chem. Inf. Comput. Sci. 23, 66–69 (1983).
Google Scholar
Belsky, A., Hellenbrandt, M., Karen, V. L. & Luksch, P. New developments in the Inorganic Crystal Structure Database (ICSD): accessibility in support of materials research and design. Acta Crystallogr. B 58, 364–369 (2002).
Google Scholar
Jain, A. et al. Commentary: the Materials Project: a materials genome approach to accelerating materials innovation. APL Mater. 1, 011002 (2013).
Google Scholar
Saal, J. E., Kirklin, S., Aykol, M., Meredig, B. & Wolverton, C. Materials design and discovery with high-throughput density functional theory: the Open Quantum Materials Database (OQMD). JOM (1989) 65, 1501–1509 (2013).
Google Scholar
Curtarolo, S. et al. AFLOW: an automatic framework for high-throughput materials discovery. Comput. Mater. Sci. 58, 218–226 (2012).
Google Scholar
Draxl, C. & Scheffler, M. NOMAD: the FAIR concept for big data-driven materials science. MRS Bull. 43, 676–682 (2018).
Google Scholar
Schmidt, J. et al. Improving machine-learning models in materials science through large datasets. Mater. Today Phys. 48, 101560 (2024).
Google Scholar
Davies, D. W. et al. Computational screening of all stoichiometric inorganic materials. Chem 1, 617–627 (2016).
Google Scholar
Riebesell, J. et al. Discovery of high-performance dielectric materials with machine-learning-guided search. Cell Rep. Phys. Sci. 5, 102241 (2024).
Google Scholar
Borg, C. K. H. et al. Quantifying the performance of machine learning models in materials discovery. Digit. Discov. 2, 327–338 (2023).
Google Scholar
Goodall, R. E. A., Parackal, A. S., Faber, F. A., Armiento, R. & Lee, A. A. Rapid discovery of stable materials by coordinate-free coarse graining. Sci. Adv. 8, eabn4117 (2022).
Google Scholar
Zhu, A., Batzner, S., Musaelian, A. & Kozinsky, B. Fast uncertainty estimates in deep learning interatomic potentials. J. Chem. Phys. 158, 164111 (2023).
Google Scholar
Depeweg, S., Hernandez-Lobato, J.-M., Doshi-Velez, F. & Udluft, S. Decomposition of uncertainty in Bayesian deep learning for efficient and risk-sensitive learning. In Proc. 35th International Conference on Machine Learning, 1184–1193 (PMLR, 2018).
Bartel, C. J. et al. A critical examination of compound stability predictions from machine-learned formation energies. NPJ Comput. Mater. 6, 1–11 (2020).
Google Scholar
Montanari, B., Basak, S. & Elena, A. Goldilocks convergence tools and best practices for numerical approximations in density functional theory calculations (EDC, 2024); https://ukerc.rl.ac.uk/cgi-bin/ercri4.pl?GChoose=gdets&GRN=EP/Z530657/1
Griffin, S. M. Computational needs of quantum mechanical calculations of materials for high-energy physics. Preprint at https://arxiv.org/abs/2205.10699 (2022).
Austin, B. et al. NERSC 2018 Workload Analysis (Data from 2018) (2022); https://portal.nersc.gov/project/m888/nersc10/workload/N10_Workload_Analysis.latest.pdf
Behler, J. & Parrinello, M. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys. Rev. Lett. 98, 146401 (2007).
Google Scholar
Bartók, A. P., Kermode, J., Bernstein, N. & Csányi, G. Machine learning a general-purpose interatomic potential for silicon. Phys. Rev. X 8, 041048 (2018).
Deringer, V. L., Caro, M. A. & Csányi, G. A general-purpose machine-learning force field for bulk and nanostructured phosphorus. Nat. Commun. 11, 5461 (2020).
Google Scholar
Zuo, Y. et al. Accelerating materials discovery with Bayesian optimization and graph deep learning. Mater. Today 51, 126–135 (2021).
Google Scholar
Chen, C. & Ong, S. P. A universal graph deep learning interatomic potential for the periodic table. Nat. Comput. Sci. 2, 718–728 (2022).
Google Scholar
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
Batatia, I., Kovács, D. P., Simm, G. N. C., Ortner, C. & Csányi, G. MACE: higher order equivariant message passing neural networks for fast and accurate force fields. Preprint at http://arxiv.org/abs/2206.07697 (2023).
Riebesell, J. Towards Machine Learning Foundation Models for Materials Chemistry. PhD Thesis, Univ. of Cambridge (2024); www.repository.cam.ac.uk/handle/1810/375689
Wu, Z. et al. MoleculeNet: a benchmark for molecular machine learning. Chem. Sci. 9, 513–530 (2018).
Google Scholar
Kpanou, R., Osseni, M. A., Tossou, P., Laviolette, F. & Corbeil, J. On the robustness of generalization of drug-drug interaction models. BMC Bioinformatics 22, 477 (2021).
Google Scholar
Meredig, B. et al. Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery. Mol. Syst. Des. Eng. 3, 819–825 (2018).
Google Scholar
Cubuk, E. D., Sendek, A. D. & Reed, E. J. Screening billions of candidates for solid lithium-ion conductors: a transfer learning approach for small data. J. Chem. Phys. 150, 214701 (2019).
Google Scholar
Zahrt, A. F., Henle, J. J. & Denmark, S. E. Cautionary guidelines for machine learning studies with combinatorial datasets. ACS Comb. Sci. 22, 586–591 (2020).
Google Scholar
Sun, W. et al. The thermodynamic scale of inorganic crystalline metastability. Sci. Adv. 2, e1600225 (2016).
Google Scholar
Goodall, R. E. A. & Lee, A. A. Predicting materials properties without crystal structure: deep representation learning from stoichiometry. Nat. Commun. 11, 6280 (2020).
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).
Chanussot, L. et al. Open Catalyst 2020 (OC20) dataset and community challenges. ACS Catal. 11, 6059–6072 (2021).
Google Scholar
Lee, K. L. K. et al. Matsciml: a broad, multi-task benchmark for solid-state materials modeling. Preprint at https://arxiv.org/abs/2309.05934 (2023).
Choudhary, K. et al. Jarvis-leaderboard: a large scale benchmark of materials design methods. NPJ Comput. Mater. 10, 93 (2024).
Google Scholar
Tran, R. et al. The Open Catalyst 2022 (OC22) dataset and challenges for oxide electrocatalysts. ACS Catal. 13, 3066–3084 (2023).
Google Scholar
Lan, J. et al. AdsorbML: a leap in efficiency for adsorption energy calculations using generalizable machine learning potentials. NPJ Comput. Mater. 9, 172 (2023).
Google Scholar
Sriram, A. et al. The Open DAC 2023 dataset and challenges for sorbent discovery in direct air capture. ACS Cent. Sci. 10, 923–941 (2024).
Google Scholar
Barroso-Luque, L. et al. Open materials 2024 (OMat24) inorganic materials dataset and models. Preprint at https://arxiv.org/abs/2410.12771 (2024).
Lilienfeld, O. A. V. & Burke, K. Retrospective on a decade of machine learning for chemical discovery. Nat. Commun. 11, 4895 (2020).
Google Scholar
Merchant, A. et al. Scaling deep learning for materials discovery. Nature 624, 80–85 (2023).
Google Scholar
Yang, H. et al. MatterSim: a deep learning atomistic model across elements, temperatures and pressures. Preprint at https://arxiv.org/abs/2405.04967 (2024).
McDermott, M. J., Dwaraknath, S. S. & Persson, K. A. A graph-based network for predicting chemical reaction pathways in solid-state materials synthesis. Nat. Commun. 12, 3097 (2021).
Google Scholar
Aykol, M., Montoya, J. H. & Hummelshøj, J. Rational solid-state synthesis routes for inorganic materials. J. Am. Chem. Soc. 143, 9244–9259 (2021).
Google Scholar
Wen, M. et al. Chemical reaction networks and opportunities for machine learning. Nat. Comput. Sci. 3, 12–24 (2023).
Google Scholar
Yuan, E. C.-Y. et al. Analytical ab initio Hessian from a deep learning potential for transition state optimization. Nat. Commun. 15, 8865 (2024).
Google Scholar
Aykol, M., Dwaraknath, S. S., Sun, W. & Persson, K. A. Thermodynamic limit for synthesis of metastable inorganic materials. Sci. Adv. 4, eaaq0148 (2018).
Google Scholar
Shoghi, N. et al. From molecules to materials: pre-training large generalizable models for atomic property prediction. Preprint at https://arxiv.org/abs/2310.16802 (2023).
Wang, H.-C., Botti, S. & Marques, M. A. L. Predicting stable crystalline compounds using chemical similarity. NPJ Comput. Mater. 7, 1–9 (2021).
Google Scholar
Cheetham, A. K. & Seshadri, R. Artificial intelligence driving materials discovery? Perspective on the article: scaling deep learning for materials discovery. Chem. Mater. 36, 3490–3495 (2024).
Google Scholar
Batatia, I. et al. A foundation model for atomistic materials chemistry. Preprint at https://arxiv.org/abs/2401.00096 (2023).
Deng, B. et al. Systematic softening in universal machine learning interatomic potentials. NPJ Comput. Mater. 11, 9 (2025).
Google Scholar
Póta, B., Ahlawat, P., Csányi, G. & Simoncelli, M. Thermal conductivity predictions with foundation atomistic models. Preprint at https://arxiv.org/abs/2408.00755 (2024).
Fu, X. et al. Forces are not enough: benchmark and critical evaluation for machine learning force fields with molecular simulations. Transact. Mach. Learn. Res. https://openreview.net/forum?id=A8pqQipwkt (2023).
Chiang, Y. et al. MLIP arena: advancing fairness and transparency in machine learning interatomic potentials through an open and accessible benchmark platform. AI for Accelerated Materials Design – ICLR 2025 https://openreview.net/forum?id=ysKfIavYQE (2025).
Li, K., DeCost, B., Choudhary, K., Greenwood, M. & Hattrick-Simpers, J. A critical examination of robustness and generalizability of machine learning prediction of materials properties. NPJ Comput. Mater. 9, 55 (2023).
Google Scholar
Li, K. et al. Exploiting redundancy in large materials datasets for efficient machine learning with less data. Nat. Commun. 14, 7283 (2023).
Google Scholar
Bitzek, E., Koskinen, P., Gähler, F., Moseler, M. & Gumbsch, P. Structural relaxation made simple. Phys. Rev. Lett. 97, 170201 (2006).
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
Glawe, H., Sanna, A., Gross, E. K. U. & Marques, M. A. L. The optimal one dimensional periodic table: a modified Pettifor chemical scale from data mining. New J. Phys. 18, 093011 (2016).
Google Scholar
Parackal, A. S., Goodall, R. E., Faber, F. A. & Armiento, R. Identifying crystal structures beyond known prototypes from x-ray powder diffraction spectra. Phys. Rev. Mater. 8, 103801 (2024).
Google Scholar
Liao, Y.-L., Wood, B., Das, A. & Smidt, T. EquiformerV2: improved equivariant transformer for scaling to higher-degree representations. International Conference on Learning Representations (ICLR) https://openreview.net/forum?id=mCOBKZmrzD (2024).
Liao, Y.-L., Smidt, T., Shuaibi, M. & Das, A. Generalizing denoising to non-equilibrium structures improves equivariant force fields. Preprint at https://arxiv.org/abs/2403.09549 (2024).
Liao, Y.-L. & Smidt, T. Equiformer: equivariant graph attention transformer for 3D atomistic graphs. International Conference on Learning Representations (ICLR) https://openreview.net/forum?id=KwmPfARgOTD (2023).
Passaro, S. & Zitnick, C. L. Reducing SO(3) convolutions to SO(2) for efficient equivariant GNNs. Preprint at https://arxiv.org/abs/2302.03655 (2023).
Neumann, M. et al. Orb: a fast, scalable neural network potential. Preprint at https://arxiv.org/abs/2410.22570 (2024).
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).
Google Scholar
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O. & Dahl, G. E. Neural message passing for quantum chemistry. In International Conference on Machine Learning (eds Precup, D. & Teh, Y. W.) 1263–1272 (PMLR, 2017).
Batzner, S. et al. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nat. Commun. 13, 2453 (2022).
Google Scholar
Thomas, N. et al. Tensor field networks: rotation- and translation-equivariant neural networks for 3D point clouds. Preprint at http://arxiv.org/abs/1802.08219 (2018).
Drautz, R. Atomic cluster expansion for accurate and transferable interatomic potentials. Phys. Rev. B 99, 014104 (2019).
Google Scholar
Choudhary, K. & DeCost, B. Atomistic line graph neural network for improved materials property predictions. NPJ Comput. Mater. 7, 1–8 (2021).
Google Scholar
Choudhary, K. et al. Unified graph neural network force-field for the periodic table: solid state applications. Digit. Discov. 2, 346–355 (2023).
Google Scholar
Xie, T. & Grossman, J. C. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys. Rev. Lett. 120, 145301 (2018).
Google Scholar
Gibson, J., Hire, A. & Hennig, R. G. Data-augmentation for graph neural network learning of the relaxed energies of unrelaxed structures. NPJ Comput. Mater. 8, 1–7 (2022).
Google Scholar
Vaswani, A. et al. Attention is all you need. In Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017); https://proceedings.neurips.cc/paper_files/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html
Ward, L. et al. Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations. Phys. Rev. B 96, 024104 (2017).
Google Scholar
Ward, L., Agrawal, A., Choudhary, A. & Wolverton, C. A general-purpose machine learning framework for predicting properties of inorganic materials. NPJ Comput. Mater. 2, 1–7 (2016).
Google Scholar
Rupp, M., Tkatchenko, A., Müller, K.-R. & Lilienfeld, O. A. V. Fast and accurate modeling of molecular atomization energies with machine learning. Phys. Rev. Lett. 108, 058301 (2012).
Google Scholar
Schütt, K. T. et al. How to represent crystal structures for machine learning: towards fast prediction of electronic properties. Phys. Rev. B 89, 205118 (2014).
Google Scholar
Riebesell, J. & Goodall, R. Matbench discovery: WBM dataset. Figshare https://figshare.com/articles/dataset/22715158 (2023).
Riebesell, J. & Goodall, R. Mp ionic step snapshots for matbench discovery. Figshare https://figshare.com/articles/dataset/23713842 (2023).
Riebesell, J. et al. janosh/matbench-discovery: v1.3.1. Zenodo https://doi.org/10.5281/zenodo.13750664 (2024).
Don’t miss more hot News like this! Click here to discover the latest in AI news!
2025-06-23 00:00:00
