iamrp.net-D7E0-1791483545-l805247528 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 Claire Martin Julien Martin Sophie Bernard 2022 10.68159/l805247528 https://iamrp.net/pub/journal/2/article/l805247528 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(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(83). 10.1038/s41524-019-0221-0 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 Musil F, Grisafi A, Bartók AP, Ortner C, Csányi G, Ceriotti M. Physics-inspired structural representations for molecules and materials. Chem Rev. 2021;121(16):9759-815. 10.1021/acs.chemrev.1c00021 Deringer VL, Bartók AP, Bernstein N, Wilkins DM, Ceriotti M, Csányi G. Gaussian process regression for materials and molecules. Chem Rev. 2021;121(16):10073-141. 10.1021/acs.chemrev.1c00022 Pilania G, Gubernatis JE, Lookman T. Multi-fidelity machine learning models for accurate bandgap predictions of solids. Comput Mater Sci. 2017;129:156-63. 10.1016/j.commatsci.2016.12.004 Jablonka KM, Ongari D, Moosavi SM, Smit B. Big-Data science in porous materials: Materials genomics and machine learning. Chem Rev. 2020;120(16):8066-129. 10.1021/acs.chemrev.0c00004 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 Baird SG, Diep TQ, Sparks TD. DiSCoVeR:A materials discovery screening tool for high performance, unique chemical compositions. Digit Discov. 2022;1(2):226-40. 10.1039/d1dd00028d Omprakash P, Manikandan B, Sandeep A, Shrivastava R, Viswesh P, Panemangalore DB. Graph representational learning for bandgap prediction in varied perovskite crystals. Comput Mater Sci. 2021;196(110530). 10.1016/j.commatsci.2021.110530 Wen C, Zhang Y, Wang C, Xue D, Bai Y, Antonov S, et al. Machine learning assisted design of high entropy alloys with low partial molar strain energies. Acta Mater. 2019;170:109-17. 10.1016/j.actamat.2019.03.033 Zong H, Ding X, Sha G, Lookman T, Sun J. Dramatically enhanced combination of ultimate tensile strength and electric conductivity of alloys via machine learning screening. Acta Mater. 2020;200:803-10. 10.1016/j.actamat.2020.09.049 Unke OT, Chmiela S, Sauceda HE, Gastegger M, Poltavsky I, Schütt KT, et al. Machine learning force fields. Chem Rev. 2021;121(16):10142-86. 10.1021/acs.chemrev.0c01111 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 Antunes LMM, Grau-Crespo R, Butler KT. Distributed representations of atoms and materials for machine learning. npj Comput Mater. 2022;8(44). 10.1038/s41524-022-00729-3 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(59). 10.1038/s41524-022-00734-6 Gong W, Yan Q. Graph-based deep learning frameworks for molecules and solid-state materials. Comput Mater Sci. 2021;195(110332). 10.1016/j.commatsci.2021.110332 Gupta T, Zaki M, Krishnan NMA, Mausam. MatSciBERT: A materials domain language model for text mining and information extraction. npj Comput Mater. 2022;8(102). 10.1038/s41524-022-00784-w Goeßmann A, Podloucky R, Draxl R. Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning. npj Comput Mater. 2022;8(41). 10.1038/s41524-022-00721-x Pettersson L, Verdozzi C. Crystal graph attention networks for the prediction of stable materials. Sci Adv. 2021;7(49):eabi7948. 10.1126/sciadv.abi7948 Kim E, Huang K, Tomala A, Matthews S, Strubell E, Saunders A, et al. Machine-learned metrics for predicting the likelihood of success in materials discovery. npj Comput Mater. 2020;6(1):193. 10.1038/s41524-020-00470-1 Wang AY-T, Kauwe SK, Murdock RJ, Sparks TD. Compositionally restricted attention-based network for materials property predictions. npj Comput Mater. 2021;7(1):77. 10.1038/s41524-021-00545-0 Zong H, Pilania G, Ding X, Ackland GJ, Asta M. Developing an interatomic potential for the W-Be system. Acta Mater. 2018;144:217-27. 10.1016/j.actamat.2017.10.050 Moran MJ, Gaultois MW, Gusev VV, Rosseinsky MJ. Deep learning for the autonomous and high-throughput analysis of X-ray absorption spectra. Digit Discov. 2022;1(6):829-40. 10.1039/d2dd00075a Sparks TD, Kauwe SK, Parry ME, Welker AC, Diep TQ. Machine learning for high-throughput experimental exploration of metal halide perovskites. Digi Discov. 2022;1(6):841-51. 10.1039/d2dd00073e Baird SG, Tran ED, Sparks TD. Modeling the solubility of CO2 in poly(ionic liquids) with machine learning. Digit Discov. 2022;1(4):452-61. 10.1039/d2dd00003d Furat O, Wang M, Finkbeiner F, Petrich L, Weber S, Kröger O, et al. Deep learning for the automation of particle analysis in catalyst layers for polymer electrolyte fuel cells. npj Comput Mater. 2019;5(1):73. 10.1038/s41524-019-0202-3 Zhou Q, Tang P, Liu S, Pan J, Yan Q, Zhang SC. Learning atoms for materials discovery. Nat Commun. 2018;9(1):3385. 10.1038/s41467-018-05816-3