iamrp.net-CUmF-1788874726-o494214876 1788874726 Institute for Advanced Materials Research Press info@iamrp.net Institute for Advanced Materials Research Press Journal of Artificial Intelligence for Materials Science J. Artif. Intell. Mater. Sci. 3149-8957 2023 2 2 Maria Hernandez Carlos Vega Lucia Torres 2023 10.68159/o494214876 https://iamrp.net/pub/journal/1/article/o494214876 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 Kusne AG, Yu H, Wu C, Zhang H, Hattrick-Simpers J, DeCost B, et al. On-the-fly closed-loop materials discovery via bayesian active learning. Nat Commun. 2020;11(1):5966. 10.1038/s41467-020-19597-w Zhong X, Gallagher B, Liu S, Kailkhura B, Hiszpanski A, Han TYJ. Explainable machine learning in materials science. npj Comput Mater. 2022;8(1):204. 10.1038/s41524-022-00884-7 Gupta V, Choudhary K, Tavazza F, Campbell C, Liao WK, 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 Merchant A, Batzner S, Schoenholz SS, Aykol M, Cheon G, Cubuk ED. Scaling deep learning for materials discovery. Nature. 2023;624(7990):80-5. 10.1038/s41586-023-06735-9 Hiszpanski AM, Gallagher B, Chellappan K, Li P, Liu S, Kim H, et al. Nanomaterial synthesis insights from machine learning of scientific articles by extracting, structuring, and visualizing knowledge. J Chem Inf Model. 2020;60(6):2876-87. 10.1021/acs.jcim.0c00199 Wang AY-T, Murdock RJ, Kauwe SK, Bukhvalov A, Hu A, Mishra S, et al. Machine learning for materials scientists: An introductory guide toward best practices. Chem Mater. 2020;32(12):4954-65. 10.1021/acs.chemmater.0c01907 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/s41524-020-00352-0 Court CJ, Yildirim B, Jain SS, Cole JM. 3-d inorganic crystal structure generation and property prediction via generative adversarial networks. J Chem Inf Model. 2020;60(6):2702-18. 10.1021/acs.jcim.0c00464 Mao Y, Hasan M, Paul A, Gupta V, Choudhary K, Tavazza F, et al. An ai-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems. npj Comput Mater. 2023;9(1):111. 10.1038/s41524-023-01061-0 Kim K, Kang S, Yoo J, Kwon Y, Nam Y, Song D, et al. Deep learning model for prediction and visualization of conformation in organic molecules and its application to organic electronics. npj Comput Mater. 2022;8(1):130. 10.1038/s41524-022-00816-5 Oviedo F, Ferres JL, Buonassisi T, Butler KT. Interpretable and explainable machine learning for materials science and chemistry. Acc Mater Res. 2022;3(5):597-607. 10.1021/accountsmr.1c00244 Esterhuizen J, Goldsmith BR, Linic S. Interpretable machine learning for knowledge extraction from heterogeneous catalyst data. Chem. 2020;6(11):3100-17. 10.1016/j.chempr.2020.10.001 Korolev VV, Mitrofanov A, Marchenko EI, Eremin NN, Tkachenko V. Interpretable machine learning in solid-state physics. Chem Mater. 2020;32(18):7822-31. 10.1021/acs.chemmater.0c02468 Jiménez-Luna J, Skalic M, Weskamp N, Schneider G. Coloring molecules with explainable artificial intelligence for preclinical relevance assessment. J Chem Inf Model. 2021;61(3):1083-94. 10.1021/acs.jcim.0c01344 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 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 Pilania G, Mannodi-Kanakkithodi A, Uberuaga BP, Ramprasad R, Gubernatis JE, Lookman T. Machine learning bandgaps of double perovskites. Sci Rep. 2016;6:19375. 10.1038/srep19375 Aykol M, Montoya JH, Hummelshøj J. Rational solid-state synthesis routes for inorganic materials. J Am Chem Soc. 2021;143(24):9244-59. 10.1021/jacs.1c04888 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 Morgan D, Jacobs R. Opportunities and challenges for machine learning in materials science. Annu Rev Mater Res. 2020;50:71-103. 10.1146/annurev-matsci-070218-010015 Jacobs R. Deep learning object detection in materials science: Current state and future directions. Comput Mater Sci. 2022;211:111527. 10.1016/j.commatsci.2022.111527 Unni R. Advancing materials science through next-generation machine learning. MRS Bull. 2025;50:1212-24. 10.1557/s43577-025-00959-y Wang K, Gupta V, Lee CS, Mao Y, Kilic MNT, Li Y, et al. Xelemnet: Towards explainable ai for deep neural networks in materials science. Sci Rep. 2024;14(1):25178. 10.1038/s41598-024-76535-2 Ko H, Lee S, Lee J. Explainable ai for material property prediction based on energy cloud: A shapley-driven xai approach. Materials. 2023;16(6):2422. 10.3390/ma16062422 Qayyum F, Kim DH, Bong SJ, Chi SJ, Choi YH. Ai for material analysis and discovery. Materials. 2022;15(4):1428. 10.3390/ma15041428 Deffrennes G, Terayama K, Abe T, Tamura R. A machine learning-based classification approach for phase diagram prediction. Mater Des. 2022;215:110497. 10.1016/j.matdes.2022.110497 Koizumi A, Deffrennes G, Terayama K, Tamura R. Performance of uncertainty-based active learning for efficient approximation of black-box functions in materials science. Sci Rep. 2024;14(1):27019. 10.1038/s41598-024-76800-4 Bello IT, Taiwo R, Esan OC, Adegoke AH, Ijaola AO, Li Z, et al. Ai-enabled materials discovery for advanced ceramic electrochemical cells. Energy AI. 2024;16:100376. 10.1016/j.egyai.2024.100376 Liu B, Wang Y, Huang Y. Explainable artificial intelligence (xai) for material design and engineering applications: A quantitative computational framework. Mater Sci Eng B. 2025;316:116651. 10.1016/j.mseb.2025.116651