iamrp.net-qavz-1791483545-q756934977 1791483545 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 2022 1 1 Hiroshi Tanaka Yuki Sato Kenji Mori Rina Okabe 2022 10.68159/q756934977 https://iamrp.net/pub/journal/2/article/q756934977 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. 10.1038/s41524-017-0056-5 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 Tao Q, Xu P, Li M, Lu W. Machine learning for perovskite materials design and discovery. npj Comput Mater. 2021;7(1):23. 10.1038/s41524-021-00493-1 Hara P, Puggioni D, Rondinelli JM. High-throughput density functional perturbation theory and machine learning predictions of infrared, piezoelectric, and dielectric responses. npj Comput Mater. 2020;6(1):65. 10.1038/s41524-020-0337-2 Chan H, Cherukara MJ, Loeffler TD, Narayanan B, Sankaranarayanan SKRS. Machine learning enabled autonomous microstructural characterization in 3D samples. npj Comput Mater. 2020;6(1):25. 10.1038/s41524-020-0287-6 Du P, Zeng H, Zhao C, Lu L, Guo L. Microstructure design using graphs. npj Comput Mater. 2021;7(1):22. 10.1038/s41524-021-00490-4 Zhang Y, Ling C. A strategy to apply machine learning to small datasets in materials science. npj Comput Mater. 2018;4(1):25. 10.1038/s41524-018-0081-2 Kumar JN, Li Q, Tang KYT, Buonassisi T, Gonzalez MA, Allen JM. Machine learning enables polymer cloud-point engineering via inverse design. npj Comput Mater. 2019;5(1):52. 10.1038/s41524-019-0191-2 Jacobs R, Mayeshiba T, Afflerbach B, Miles L, Williams M, Turner A, et al. The materials simulation toolkit for machine learning (MAST-ML): An automated open source toolkit to accelerate data-driven materials research. Comput Mater Sci. 2020;175:109599. 10.1016/j.commatsci.2020.109599 Lu S, Zhou Q, Guo Y, Zhang Y, Wu Y, Wang J. Graph convolutional neural networks improve accuracy of defect classification in crystal structures. npj Comput Mater. 2020;6(1):160. 10.1038/s41524-020-00427-z Kaufmann K, Maryanovsky D, Mellor WM, Zhu C, Rosengarten AS, Harrington TJ, Oses C, et al. Discovery of high-entropy ceramics via machine learning. npj Comput Mater. 2020;6(1):42. 10.1038/s41524-020-0317-6 Saidi WA, Shadid W, Veser G. Deep learning in electron microscopy. npj Comput Mater. 2021;7(1):56. 10.1038/s41524-021-00523-y Lu H-J, Zou N, Jacobs R, Afflerbach B, Lu X-G, Morgan D. Error assessment and optimal cross-validation approaches in machine learning applied to impurity diffusion. Comput Mater Sci. 2019;169:109075. 10.1016/j.commatsci.2019.109075 Xue D, Balachandran PV, Yuan R, Hu T, Qian X, Dougherty ER, et al. Accelerated search for biomimetic materials with machine learning. npj Comput Mater. 2021;7(1):18. 10.1038/s41524-021-00491-3 Kaufmann K, Vecchio KS. Searching for high entropy alloys: A machine learning approach. Acta Mater. 2020;198:231-40. 10.1016/j.actamat.2020.07.054 Huang W, Martin P, Zhuang HL. Machine-learning phase prediction of high-entropy alloys. Acta Mater. 2019;169:225-36. 10.1016/j.actamat.2019.03.012 Möller JJ, Körner W, Krugel G, Urban DF, Elsässer C. Compositional optimization of hard-magnetic phases with machine-learning models. Acta Mater. 2018;153:53-61. 10.1016/j.actamat.2018.04.028 Alcobaça E, Mastelini SM, Botari T, Pimentel BA, Cassar DR, de Carvalho et al. Explainable machine learning algorithms for predicting glass transition temperatures. Acta Mater. 2020;188:92-102. 10.1016/j.actamat.2019.12.037 Zhang H, Fu H, Zhu S, Yong W, Sun J, He Y, et al. Machine learning assisted composition effective design for precipitation strengthened copper alloys. Acta Mater. 2021;215:117118. 10.1016/j.actamat.2021.117118 He J, Li J, Liu C, Wang C, Wang X, Liu B, et al. Machine learning identified materials descriptors for ferroelectricity. Acta Mater. 2021;209:116815. 10.1016/j.actamat.2021.116815 Zou C, Li J, Wang WY, Zhang D, Froese R, Tang D, et al. Integrating data mining and machine learning to discover high-strength ductile titanium alloys. Acta Mater. 2021;202:211-21. 10.1016/j.actamat.2020.10.056 Curnan MT, Saidi WA, Yang JC, Han JW. Grain boundary mobilities in polycrystals: A data-driven approach. Acta Mater. 2021;209:116804. 10.1016/j.actamat.2021.116804 Keith JA, Vassilev-Galindo V, Cheng B, Chmiela S, Gastegger M, Müller KR, et al. Combining machine learning and computational chemistry for predictive insights into chemical systems. Chem Rev. 2021;121(15):9816-72. 10.1021/acs.chemrev.1c00107 Boyd PG, Lee Y, Smit B. Computational development of the nanoporous materials genome in search of novel hyper-confined fluids. Chem Rev. 2017;117(13):9145-65. 10.1021/acs.chemrev.7b00098 Flores-Leonar MM, Mejía-Mendoza LM, Domratcheva T, Farias-Rodríguez V, Vargas R, Aspuru-Guzik A, et al. High-throughput experimentation meets artificial intelligence: A new pathway to the discovery of materials. Nat Mach Intell. 2020;2(7):367-76. 10.1038/s42256-020-0203-8 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. 10.1038/s41467-018-05761-6 Ren Z, Tian SIP, Noh J, Oviedo F, Xing G, Li J, et al. Inverse design of solid-state materials via a continuous representation. Matter. 2019;1(6):1528-42. 10.1016/j.matt.2019.08.017 Tian SIP, Kim SY, McDermott MJ, Bartel CJ, Ceder G. Data-driven materials discovery from large chemistry spaces. Matter. 2020;3(4):961-3. 10.1016/j.matt.2020.08.019 Wu CT, Chang HT, Wu CY, Chen SW, Huang SY, Liu M, et al. Machine learning recommends affordable new Ti alloy with bone-like modulus. Mater Today. 2020;34:41-50. 10.1016/j.mattod.2019.12.003 Juneja R, Singh AK. Machine learning for advanced functional materials. Adv Intell Syst. 2020;2(6):2000023. 10.1002/aisy.202000023 Xiao D, Hu C, Du L, Chen C, Pan J, Chen G. Machine learning for materials research and development. Adv Intell Syst. 2021;3(6):2000235. 10.1002/aisy.202000235 Dan Y, Zhao T, Zhang X, Zhao H, Li X, Li D. Data-driven construction of a design space for two-stage bulk gas separation processes. npj Comput Mater. 2018;4(1):64. 10.1038/s41524-018-0114-z