iamrp.net-uBjv-1791483544-a645078092 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 2023 2 1 Hiroshi Tanaka Yuki Sato Kenji Mori 2023 10.68159/a645078092 https://iamrp.net/pub/journal/2/article/a645078092 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. 10.1038/s41524-019-0221-0 Mazhnik E, Oganov AR. Machine learning directed search for ultraincompressible, superhard materials. npj Comput Mater. 2019;5(1):1-7. Chen L, Tran H, Batra R, Kim C, Ramprasad R. Machine learning models for the prediction of effective thermal properties of porous media. npj Comput Mater. 2019;5(1):1-8. Tao Q, Xu P, Li M, Lu W. Machine learning for perovskite materials design and discovery. npj Comput Mater. 2021;7(1):23. Janet JP, Duan C, Kulik HJ. Uncertainty quantification in molecular property prediction using faith-based artificial neural networks. npj Comput Mater. 2022;8(1):1-13. Chen G, Tao X, Hajfathalian M, Cao G, Chen Z, Wang J, et al. A critical examination of robustness and generalizability of machine learning prediction of materials properties. npj Comput Mater. 2023;9(1):55. Konstantakopoulos M, Fadel ER, Prost V, Mannan S, Malgras V, Hayashi K, et al. Interpretable discovery of semiconductors with machine learning. npj Comput Mater. 2023;9(1):1-10. Jennings PC, Lysgaard S, Hummelshøj JS, Vegge T, Bligaard T. Genetic algorithms for computational materials discovery accelerated by machine learning. npj Comput Mater. 2019;5(1):46. Pilania G, Gubernatis JE, Lookman T. Multi-fidelity machine learning models for accurate bandgap predictions of solids. Comput Mater Sci. 2017;129:156-63. Meredig B, Antono E, McCulloch S, Rajan K, Ling Y. Evaluating explorative prediction power of machine learning algorithms for materials discovery using k-fold forward cross-validation. Comput Mater Sci. 2020;171:109203. Li W, Jacobs R, Morgan D. Predicting the thermodynamic stability of perovskite oxides using machine learning models. Comput Mater Sci. 2018;150:454-63. 10.1016/j.commatsci.2018.04.033 Elton DC, Boukouvalas Z, Fuge MD, Chung PW. Deep learning for molecular design—a review of the state of the art. Comput Mater Sci. 2019;162:433-46. Kondo R, Yamakawa S, Masuoka Y, Tajima S, Asahi R. Microstructure recognition using convolutional neural networks for prediction of ionic conductivity in ceramics. Acta Mater. 2017;141:29-38. 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 materials design. Nat Commun. 2020;11(1):1-9. Lu S, Zhou Q, Ouyang Y, Guo Y, Li Q, Wang J. Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning. Nat Commun. 2018;9(1):3405. Zhong W, Yan K, Liu H, Zhao X, Tang Y, Liu X, et al. Machine learning guided discovery of barrierless transitions in two-dimensional heterogeneous catalysis. Nat Commun. 2022;13(1):1-9. Lu X, Qian P, Chen N, He Z, Wang X, Wu J, et al. Machine learning accelerated discovery of functional glass via human-machine collaboration. Nat Commun. 2022;13(1):1-11. Sumita M, Yang X, Ishihara S, Tamura R, Tsuda K. Machine learning in materials discovery: Confirmed efficient synthesis of new organic luminophores. Nat Commun. 2018;9(1):1-7. Zakutayev A, Wunder N, Schwarting M, Perkins JD, White R, Munch K, et al. An open experimental database for exploring inorganic materials. Sci Adv. 2018;4(4):eaaq1566. Ren F, Ward L, Williams T, Laws KJ, Wolverton C, Hattrick-Simpers J, et al. Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments. Sci Adv. 2018;4(4):eaat6049. Bartók AP, De S, Poelking C, Bernstein N, Kermode JR, Csányi G, et al. Machine learning unifies the modeling of materials and molecules. Sci Adv. 2017;3(12):e1701816. Dave A, Mitchell J, Kandasamy K, Wang H, Burke B, Paria B, et al. Autonomous discovery of battery electrolytes with robotic experimentation and machine learning. Cell Rep Phys Sci. 2020;1(12):100264. Keith JA, Vassilev-Galindo V, Cheng B, Chmiela S, Gastegger M, Müller K-R, et al. Combining machine learning and computational chemistry for predictive insights into chemical systems. Chem Rev. 2021;121(15):9816-72. Borg CKH, Muckley ES, Nyby C, Saal JE, Ward L, Mehta A, et al. Quantifying the performance of machine learning models in materials discovery. Digit Discov. 2023;2(2):327-38. Yang L, Yang L, Fan G, Ma W, Zheng S, Mavromatis V, et al. Machine learning-enabled high-throughput material screening for MOF-based membrane gas separation. Digit Discov. 2023;2(2):415-27. Xia W, Song J, Chen H, Han Z, Zhao G, Zhang J, et al. Accelerating materials discovery using integrated deep machine learning approaches. J Mater ChemA. 2023;11(48):25973-82. 10.1039/D3TA03771A Abolhasani M, Kumara KSRN. Autonomous materials synthesis by machine learning and robotics. Nat Mach Intell. 2023;5(1):4-6. Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A. Machine learning for molecular and materials science. Nature. 2018;559(7715):547-55. 10.1038/s41586-018-0337-2