From High-Throughput Computation to Autonomous Discovery: A Review of Closed-Loop Data Infrastructures in Materials Engineering

iamrp.net-Kams-1791515266-h935604128 1791515266 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 From High-Throughput Computation to Autonomous Discovery: A Review of Closed-Loop Data Infrastructures in Materials Engineering Ravi Kumar Neha Sharma Aniket Deshmukh 2022 10.68159/h935604128 https://iamrp.net/pub/journal/2/article/h935604128 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 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 Chen C, Ye W, Zuo Y, Zheng C, Ong SP. Graph networks as a universal machine learning framework for molecules and crystals. Chem Mater. 2019;31(9):3564-72. 10.1021/acs.chemmater.9b01294 Schleder GR, Padilha ACM, Acosta CM, Costa M, Fazzio A. From DFT to machine learning: Recent approaches to materials science–a review. J Phys Mater. 2019;2(3):032001. 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):9759-9815. 10.1021/acs.chemrev.1c00021 Louis SY, Zhao Y, Nasiri A, Wang X, Song Y, Liu F, et al. Graph convolutional neural networks with global attention for improved materials property prediction. Phys Chem Chem Phys. 2020;22(32):18141-8. 10.1039/D0CP01474E Chen C, Ong SP. Atomistic line graph neural network for improved materials property predictions. npj Comput Mater. 2021;7(1):141. 10.1038/s41524-021-00617-0 Fung V, Hu G, Ganesh P, Sumpter BG. Benchmarking graph neural networks for materials chemistry. npj Comput Mater. 2021;7(1):84. 10.1038/s41524-021-00554-0 Dan Y, Zhao Y, Li X, Li S, Hu M, Hu J. Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials. npj Comput Mater. 2020;6(1):84. 10.1038/s41524-020-00352-y Wu D, Fang F, Wang Y, Guo X, Zhao Y, Duan Y, et al. Machine-Learning microstructure for inverse material design. Adv Mater. 2021;33(29):2101207. 10.1002/adma.202101207 Chen L, Zhang W, Nie Z, Li S, Pan F. Generative models for inverse design of inorganic solid materials. J Mater Inform. 2021;1(1):7. 10.20517/jmi.2021.07 Oviedo F, Ferres JL, Agarwal TK, Cai J, Wang E, Lipkowitz G, et al. Interpretable and explainable machine learning models for materials discovery. npj Comput Mater. 2022;8(1):115. 10.1038/s41524-022-00809-2 Kaufmann K, Maryanovsky D, Mellor WM, Zhu C, Rosengarten AS, Vecchio KS. Discovery of high-entropy ceramics via machine learning. npj Comput Mater. 2020;6(1):42. 10.1038/s41524-020-0317-6 Momeni K, Ji HM, Ji Y, Hood RW, Hao J, Hu K, et al. Multiscale computational understanding and growth of 2D materials: A review. npj Comput Mater. 2020;6(1):97. 10.1038/s41524-020-00367-5