iamrp.net-lnZL-1791483544-a985961540 1791483544 Institute for Advanced Materials Research Press info@iamrp.net Institute for Advanced Materials Research Press Journal of Computational and Data-Driven Materials Engineering J. Comput. Data-Driven Mater. Eng. 3149-9368 2024 3 1 Daniel Brooks Ethan Moore Amelia Carter 2024 10.68159/a985961540 https://iamrp.net/pub/journal/2/article/a985961540 Ramprasad R, Batra R, Pilania G, Mannodi-Kanakkithodi A, Kim C. Machine learning in materials informatics: Recent applications and prospects. npj Comput Mater. 2017;3(1):54. Schmidt J, Marques MRG, Botti S, Marques MAL. Recent advances and applications of machine learning in solid-state materials science. npj Comput Mater. 2019;5(1):83. Morgan D, Jacobs R. Opportunities and challenges for machine learning in materials science. Ann Rev Mater Res. 2020;50:71-103. Schmidt J, Shi R, Berri S, Chen L, Botti S, Marques MAL. Predicting the thermodynamic stability of solids with machine learning based on the API automatic FLOW for materials discovery (AFLOW). Chem Mater. 2017;29(17):7354-64. Wen C, Zhang Y, Wang C, Xue D, Bai Y, Antonov S, et al. Machine learning assisted design of high entropy alloys with desired property. Acta Mater. 2019;170:109-17. Pilania G, Gubernatis JE, Lookman T. Multi-fidelity machine learning models for accurate bandgap predictions of solids. Comput Mater Sci. 2017;129:156-63. Luo B, Li Y, Zhang S, Feng Y, Zhang D, Shi Z, et al. Explainable machine learning in materials science. npj Comput Mater. 2022;8(1):204. Chen C, Zuo Y, Ye W, Li X, Ong SP. Learning properties of ordered and disordered materials from multi-fidelity data. Nat Comput Sci. 2021;1(1):46-53. Chen C, Ye W, Zuo Y, Zheng C, Ong SP. Graph networks as a universal machine learning framework for molecules and crystals. Chem Mater. 2019;31(9):3564-72. Fung V, Zhang J, Juarez E, Sumpter BG. Benchmarking graph neural networks for materials chemistry. npj Comput Mater. 2021 Jun 3;7(1):84. Dai M, Demirel MF, Liang Y, Hu JM. Graph neural networks for an accurate and interpretable prediction of the properties of polycrystalline materials. npj Comput Mater. 2021;7(1):103. Dan Y, Zhao Y, Li X, Li S, Hu M, Hu J. Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials. npj Comput Mater. 2020;6(1):84. Yang C, Zhang Y, Wen C, Yin K, Zhao Y, Su Y. Machine learning-based alloy design system for facilitating the rational design of high entropy alloys with enhanced hardness. Acta Mater. 2022;222:117431. Tran K, Ulissi ZW. Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution. Nat Catal. 2018;1(9):696-703. Zhang H, Zhao Y, Wang C, Su Y. Interpretable machine-learning strategy for soft-magnetic property and thermal stability in Fe-based metallic glasses. Acta Mater. 2020;200:803-10. Chen C, Zuo Y, Ye W, Li X, Deng Z, Ong SP. A critical review of machine learning of energy materials. Adv Energy Mater. 2020;10(8):1903242. de Witt CS, Peng B, Kamienny PA, Torr P, Böhmer W, Whiteson S. Deep multi-agent reinforcement learning for decentralized continuous cooperative control. arXiv preprint arXiv:2003.06709. 2020;19. Glielmo A, Zeni C, De Vita A. Efficient nonparametric n-body force fields from machine learning. Phys RevB. 2018;97(18):184307. Court CJ, Yildirim B, Jain SS, Cole JM. Paradigm shift without the shift: Interpreting and advancing deep learning for materials. Matter. 2022;5(5):1340-2. Merchant A, Batzner S, Schoenholz SS, Aykol M, Cheon G, Cubuk ED. Scaling deep learning for materials discovery. Nature. 2023;624(7990):80-5. Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A. Machine learning for molecular and materials science. Nature. 2018;559(7715):547-55. Kitchin JR. Machine learning in molecular and materials science and chemistry. Nat Comput Sci. 2021;1(5):333-4. Ward L, Dunn A, Faghaninia A, Zimmermann NER, Bajaj S, Wang Q, et al. Matminer: An open source toolkit for materials data mining. Comput Mater Sci. 2018;152:60-9. Bruix A, Margraf JT, Andersen M, Reuter K. First-principles-based multiscale modelling of heterogeneous catalysis. Nat Catal. 2019;2(8):659-70. Lu Z, Lu S, Zhao Y, Wang C, Su Y. Interpretable machine-learning strategy for failure prediction in ceramic matrix composites. Acta Mater. 2021;217:117177. Ward L, Agrawal A, Choudhary A, Wolverton C. A general-purpose machine learning framework for predicting properties of inorganic materials. npj Comput Mater. 2016;2(1):16028.