iamrp.net-wq6I-1791471805-a908576617 1791471805 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 Maria Gonzalez Javier Ruiz Lucia Torres 2022 10.68159/a908576617 https://iamrp.net/pub/journal/2/article/a908576617 Schmidt J, Shi H-L, Botti S, Marques MRG. Explainable machine learning in materials science. npj Comput Mater. 2022;8:204. 10.1038/s41524-022-00884-7 Schmidt J, Marques MRG, Botti S, Marques MAL. Recent advances and applications of machine learning in solid-state materials science. npj Comput Mater. 2019;5:83. 10.1038/s41524-019-0221-0 Choudhary K, DeCost B, Zeni C, White KF, Desai S, Hattrick-Simpers JR, 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 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 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 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;5:21. 10.1038/s41524-019-0153-8 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 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;8:84. 10.1038/s41524-022-00765-z 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;12:6595. 10.1038/s41467-021-26921-5 Broderick S, Aagesen L, Pilania G, Uberuaga BP, Strachan A. Analyzing machine learning models to accelerate generation of fundamental materials insights. npj Comput Mater. 2019;5:17. 10.1038/s41524-019-0172-5 Chang R, Wang Y-X, Ertekin E. Towards overcoming data scarcity in materials science: Unifying models and datasets with a mixture of experts framework. npj Comput Mater. 2022;8:242. 10.1038/s41524-022-00929-x Goodall REA, Lee AA. Predicting materials properties without crystal structure: deep representation learning from stoichiometry. Nat Commun. 2020;11:6280. 10.1038/s41467-020-19964-7 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 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;7:10. 10.1038/s41524-020-00488-z Iwasaki Y, Sawada R, Stanev V, Ishida M, Kirihara A, Omori Y, et al. Identification of advanced spin-driven thermoelectric materials via interpretable machine learning. npj Comput Mater. 2019;5(1):103. 10.1038/s41524-019-0241-9 Wagner N, Rondinelli JM. Reliable and explainable machine-learning methods for accelerated material discovery. npj Comput Mater. 2019;5:24. 10.1038/s41524-019-0248-2 Cook GB, Aykol M, Spitzner FC, Hegde VI, Afzal MA, Suram SK, et al. Machine learning in concrete science: Applications, challenges, and best practices. npj Comput Mater. 2022;8:810. 10.1038/s41524-022-00810-x 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;5:62. 10.1038/s41524-019-0204-1 Jennings PC, Lysgaard S, Hummelshøj JS, Vegge T, Bligaard T. Genetic algorithms for computational materials discovery accelerated by machine learning. npj Comput Mater. 2019;5:46. 10.1038/s41524-019-0181-4 Liu Y, Zhao T, Yang G, Ju W, Shi S. Machine learning for perovskite materials design and discovery. npj Comput Mater. 2021;7:495. 10.1038/s41524-021-00495-8 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. 10.1126/sciadv.1701816 Watson AEAA, Tropsha A. Machine learning of material properties: Predictive and interpretable multilinear models. Sci Adv. 2022;8(18):abm7185. 10.1126/sciadv.abm7185 Sun W, Zhou Q, Sun Y, Zhang Y, Chen D, Li S, et al. Machine learning–assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials. Sci Adv. 2019;5(11):eaay4275. 10.1126/sciadv.aay4275 Wahl CB, Svensson SP, Amsler M, Bigi C, Erps TB, Goedecker S, et al. Machine learning–accelerated design and synthesis of polyelemental heterostructures. Sci Adv. 2021;7(49):abj5505. 10.1126/sciadv.abj5505 Liu Y, Tao L, Huang J, McCutcheon JR, Li Y. Machine learning enables interpretable discovery of innovative polymers for gas separation membranes. Sci Adv. 2022;8(29):abn9545. 10.1126/sciadv.abn9545 Pettersson L, Verdozzi C, Santos J. Crystal graph attention networks for the prediction of stable materials. Sci Adv. 2021;7(49):eabi7948. 10.1126/sciadv.abi7948 Yao Z, Sanchez-Lengeling B, Bobbitt NS, Bucior BJ, Lee SGH, Day S, et al. Inverse design of porous materials using artificial neural networks. Sci Adv. 2020;6(1):eaax9324. 10.1126/sciadv.aax9324 Xia X, Ju S, Shi L, Xu X, Buehler MJ. Inverse design of soft materials via a deep learning–based evolutionary strategy. Sci Adv. 2022;8(4):abj6731. 10.1126/sciadv.abj6731 Chmiela S, Tkatchenko A, Sauceda HE, Poltavsky I, Schütt KT, Müller K-R. Machine learning of accurate energy-conserving molecular force fields. Sci Adv. 2017;3(5):e1603015. 10.1126/sciadv.1603015 Yu Z, Yu C-H, Buehler MJ. Deep learning model to predict complex stress and strain fields in hierarchical composites. Sci Adv. 2021;7(15):abd7416. 10.1126/sciadv.abd7416 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):eaaq1566. 10.1126/sciadv.aaq1566 Cersonsky RK, Helfrecht BA, Engel EA, Klym S, Anderson JA, Glotzer SC. Rapid discovery of stable materials by coordinate-free coarse graining. Sci Adv. 2022;8(30):abn4117. 10.1126/sciadv.abn4117