iamrp.net-soSm-1791483544-m350665699 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 1 Lucas Meyer Anna Schmid Stefan Braun 2022 10.68159/m350665699 https://iamrp.net/pub/journal/2/article/m350665699 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 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 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 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 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(1):21. 10.1038/s41524-019-0153-8 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 Chen C, Ye W, Zuo Y, Zheng C. Graph networks as a universal machine learning framework for molecules and crystals. Chem Mater. 2019;31(9):3564-72. 10.1021/acs.chemmater.9b01294 Deringer VL, Bartók AP, Proserpio DM, Day GM, Csányi G, Pickard CJ. Machine learning interatomic potentials as emerging tools for materials science. Adv Mater. 2019;31(46):1902765. 10.1002/adma.201902765 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 Zubatyuk R, Smith JS, Leszczynski J, Isayev O. Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network. Sci Adv. 2019;5(8):eaav6490. 10.1126/sciadv.aav6490 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 Rosen AS, Iyer SM, Ray D, Yao Z, Aspuru-Guzik A, Gagliardi L et al. Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery. Matter. 2021;4(5):1578-97. 10.1016/j.matt.2021.02.021 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 Hatakeyama-Sato K, Tezuka T, Ujihira W, Morita Y, Nishide H, Oyaizu K. Tackling the challenge of a huge materials science search space with quantum‐inspired annealing. Adv Intell Syst. 2021;3(2):2000209. 10.1002/aisy.202000209 Pilania G. Machine learning in materials science: From explainable predictions to autonomous design. Comput Mater Sci. 2021;193:110360. 10.1016/j.commatsci.2021.110360 Mishin Y. Machine-learning interatomic potentials for materials science. Acta Mater. 2021;214:116980. 10.1016/j.actamat.2021.116980 Dai D, Xu T, Wei X, Ding G, Xu Y, Zhang J, et al. Using machine learning and feature engineering to characterize limited material datasets of high-entropy alloys. Comput Mater Sci. 2020;175:109618. 10.1016/j.commatsci.2020.109618 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(16):9816-72. 10.1021/acs.chemrev.1c00107 Kim C, Batra R, Chen L, Tran H, Ramprasad R. Polymer design using genetic algorithm and machine learning. Comput Mater Sci. 2021;186:110067. 10.1016/j.commatsci.2020.110067 Glielmo A, Zeni C, De Vita A. Efficient nonparametric n-body force fields from machine learning. Phys Rev B. 2018;97(18):184307. 10.1103/PhysRevB.97.184307 Chen L, Tran H, Batra R, Kim C, Ramprasad R. Machine learning models for the prediction of energy, forces, and stresses for platinum. npj Comput Mater. 2021;7(1):19. 10.1038/s41524-021-00490-z Unke OT, Chmiela S, Sauceda HE, Gastegger M, Poltavsky I, Schütt KT, et al. Machine learning force fields. Chem Rev. 2021;121(16):10142-10186. 10.1021/acs.chemrev.0c01111 Himanen L, Jäger MOJ, Morooka EV, Federici Canova F, Ranawat YS, Gao DZ, et al. DScribe: Library of descriptors for machine learning in materials science. Comput Phys Commun. 2020;247:106949. 10.1016/j.cpc.2019.106949 Rosenbrock CW, Homer ER, Csányi G, Hart GLW. Discovering the building blocks of atomic systems via machine learning. npj Comput Mater. 2017;3(1):29. 10.1038/s41524-017-0034-y Haghighatlari M, Vishwakarma G, Altarawy D, Subramanian R, Kota BU, Sonpal A, et al. ChemML: A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data. Wiley Interdiscip Rev Comput Mol Sci. 2020;10(4):e1458. 10.1002/wcms.1458 Veit M, Jain SK, Bonakala S, Rudra I, Hohl D, Csányi G. Equation of state of fluid methane from first principles with machine learning potentials. J Chem Theory Comput. 2019;15(4):2574-86. 10.1021/acs.jctc.8b01242 Botu V, Batra R, Chapman J, Ramprasad R. Machine learning force fields: Construction, validation, and outlook. J Phys Chem C. 2017;121(1):511-22. 10.1021/acs.jpcc.6b10908 Rosen AS, Fung V, Huck P, O'Donnell CT, Horton MK, Trask A, et al. High-throughput predictions of metal-organic framework electronic properties: Theoretical challenges, graph neural networks, and data exploration. npj Comput Mater. 2020;6(1):112. 10.1038/s41524-020-00389-1