iamrp.net-NmNB-1791483544-a503846256 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 2022 1 2 Nguyen Thanh Huy Pham Quang Minh Le Thi Bich 2022 10.68159/a503846256 https://iamrp.net/pub/journal/2/article/a503846256 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 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 Choudhary K, DeCost B, Chen C, Jain A, Tavazza F, Cohn R, et al. Recent advances and applications of deep learning methods in materials science. npj Comput Mater. 2022;8(1):59. 10.1038/s41524-022-00734-6 Goodall REA, Lee AA. Predicting materials properties without crystal structure: deep representation learning from stoichiometry. Nat Commun. 2020;11(1):6280. 10.1038/s41467-020-19964-7 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 Feng S, Fu H, Zhou H, Wu Y, Lu Z, Dong H. A general and transferable deep learning framework for predicting phase formation in materials. npj Comput Mater. 2021;7(1):10. 10.1038/s41524-020-00488-z Pyzer-Knapp EO, Pitera JW, Staar PWJ, Takeda S, Laino T, Sanders DP, et al. Accelerating materials discovery using artificial intelligence, high performance computing and robotics. npj Comput Mater. 2022;8(1):84. 10.1038/s41524-022-00765-z Aspuru-Guzik A, Persson KA, Flores-Leonar MM, Salinas O, Allioux D, Farfan J, et al. Autonomous experimentation systems for materials development: A community perspective. Matter. 2021;4(9):2702-26. 10.1016/j.matt.2021.06.036 Uhrin M, Huber SP, Yu J, Marzari N, Pizzi G. Workflows in AiiDA: Engineering a high-throughput, event-based engine for robust and modular computational workflows. Comput Mater Sci. 2021;187:110086. 10.1016/j.commatsci.2020.110086 Zakutayev A, Wunderlich S, Schwarting M, Perkins JD, Schafer R, Snyder GJ, et al. The materials research platform: Defining the requirements from user stories. Matter. 2019;1(6):1419-30. 10.1016/j.matt.2019.10.024 Gupta V, Choudhary K, Tavazza F, Campbell C, Liao W-k, Choudhary A, et al. Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data. Nat Commun. 2021;12(1):6595. 10.1038/s41467-021-26921-5 Chang R, Wang YX, Ertekin E. Towards overcoming data scarcity in materials science: unifying models and datasets with a mixture of experts framework. npj Comput Mater. 2022;8(1):242. 10.1038/s41524-022-00929-x Sutton C, Boley M, Ghiringhelli LM, Rupp M, Vreeken J, Scheffler M. Identifying domains of applicability of machine learning models for materials science. Nat Commun. 2020;11(1):4428. 10.1038/s41467-020-17112-9 Umehara M, Stein HS, Guevarra D, Newhouse PF, Boyd DA, Gregoire JM. Analyzing machine learning models to accelerate generation of fundamental materials insights. npj Comput Mater. 2019;5(1):34. 10.1038/s41524-019-0172-5 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-z Nyshadham C, Rupp M, Bekker B, Shapeev AV, Mueller T, Rosenbrock CW, et al. Machine-learned multi-system surrogate models for materials prediction. npj Comput Mater. 2019;5(1):51. 10.1038/s41524-019-0189-9 Zhong X, Gallagher B, Liu S, Kailkhura B, Hiszpanski A, Han TYJ. Explainable machine learning in materials science. npj Comput Mater. 2022;8:204. 10.1038/s41524-022-00884-7 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/s41524-021-00495-8 Fung V, Zhang J, Hu G, Ganesh P, Sumpter BG. Inverse design of two-dimensional materials with invertible neural networks. npj Comput Mater. 2021;7(1):200. 10.1038/s41524-021-00670-x Lookman T, Balachandran PV, Xue D, Yuan R. Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design. npj Comput Mater. 2019;5(1):21. 10.1038/s41524-019-0153-8 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. 10.1038/s41524-019-0181-4 Huo H, Rong Z, Kononova O, Sun W, Botari T, He T, et al. Semi-supervised machine-learning classification of materials synthesis procedures. npj Comput Mater. 2019;5(1):62. 10.1038/s41524-019-0204-1 Yao Z, Sánchez-Lengeling B, Bobbitt NS, Bucior BJ, Lee SGH, Day GM, et al. Inverse design of nanoporous crystalline reticular materials with deep generative models. Nat Mach Intell. 2021;3:76-86. 10.1038/s42256-020-00271-1 Avery P, Wang X, Oses C, Gossett E, Proserpio DM, Toher C, et al. Predicting superhard materials via a machine learning informed evolutionary structure search. npj Comput Mater. 2019;5(1):89. 10.1038/s41524-019-0226-8 Liu J, Liu Y, Wang Z, Tang J, Wen Y, Huang J, et al. Artificial intelligence-based material discovery for clean energy future. Adv Intell Syst. 2022;4(6):2200073. 10.1002/aisy.202200073 Suwardi A, Wang F, Xue K, Han MY, Teo EY, Wang P, et al. Machine learning-driven biomaterials evolution. Adv Mater. 2022;34(1):2102703. 10.1002/adma.202102703 Brunton SL, Proctor JL, Kutz FJ. Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Sci Adv. 2017;3(4):e1602614. 10.1126/sciadv.1602614 Wolloch M, Levchenko SV, Righi MC. Computational synthesis of 2D materials: A high-throughput approach to materials design. Comput Mater Sci. 2022;204:111182. 10.1016/j.commatsci.2021.111182 Wolloch M, Losi G, Chehaimi O, Yalcin F, Ferrario M, Righi MC. High-throughput generation of potential energy surfaces for solid interfaces. Comput Mater Sci. 2022;207:111302. 10.1016/j.commatsci.2022.111302 Lv C, Chen C, Chuang YC, Tseng TL. A real-time surface inspection method for precision steel balls based on machine vision. Adv Mater. 2022;34(10):2101474. 10.1002/adma.202101474 Li Z, Yoon J, Zhang R, Rajabipour F, Srubar WV III, Dabo I, et al. Machine learning in concrete science: Applications, challenges, and best practices. npj Comput Mater. 2022;8(1):127. 10.1038/s41524-022-00810-x