iamrp.net-snmO-1792546419-t153905187 1792546419 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 2026 5 1 Hiroshi Nakamura Yuta Kato Aiko Morita 2026 10.68159/t153905187 https://iamrp.net/pub/journal/1/article/t153905187 De Breuck P-P, Evans ML, Hautier G. Machine learning in materials research: Developments over the last decade and challenges for the future. Curr Opin Solid State Mater Sci. 2024;28:101139. 10.1016/j.cossms.2024.101139 Damewood JR, Karaguesian J, Lunger JR, Tan AR, Xie M, Peng J, et al. Representations of materials for machine learning. Annu Rev Mater Res. 2023;53:399-426. 10.1146/annurev-matsci-080921-085947 Che L, He Z, Zheng K, Si T, Ge M, Cheng H, et al. Deep learning in alloy material microstructures: Application and prospects. Mater Today Commun. 2023;37:107531. 10.1016/j.mtcomm.2023.107531 Goossens N, Gong Y, Zhang C, Liu B, Luan X. A deep learning approach for complex microstructure inference. Nat Commun. 2021;12:6105. 10.1038/s41467-021-26565-5 Pei Z, Rozman KA, Doğan ÖN, Wen Y, Gao N, Holm EA, et al. Machine-learning microstructure for inverse material design. Adv Sci. 2021;8:2101207. 10.1002/advs.202101207 Wang X, Su H, Li N, Chen Y, Yang Y, Meng H. A deep learning labeling method for material microstructure image segmentation. Processes. 2023;11:3272. 10.3390/pr11123272 Zhong X, Gallagher B, Liu S, Kailkhura B, Hiszpanski A, Han TYJ. Explainable machine learning in materials science. npj Comput Mater. 2022;8:204. 10.1038/s41524-022-00884-7 Li X, Liu W, Khonsari MM, Xie L, Chen J, Sun C. Machine learning-based identification of interpretable process-structure linkages in metal additive manufacturing. Addit Manuf. 2023;71:103589. 10.1016/j.addma.2023.103589 Lu Z, Li Y, Zhang J, Wu J, Liu Y. Interpretable machine-learning strategy for soft-magnetic property and thermal stability in Fe-based metallic glasses. npj Comput Mater. 2020;6:187. 10.1038/s41524-020-00460-7 Kondo R, Yamashita T, Uesugi M, Aratani N. Interpretable deep learning approach for extracting nanoparticle features from transmission electron microscopy images. Sci Rep. 2022;12:4274. 10.1038/s41598-022-06597-5 Chowdhury A, Kautz E, Yener B, Lewis D. Image driven machine learning methods for microstructure recognition. Comput Mater Sci. 2020;171:109283. 10.1016/j.commatsci.2019.109283 DeCost BL, Francis T, Holm EA. High-throughput 3D segmentation of large-scale heterogeneous microstructure data using convolutional neural networks. Microsc Microanal. 2021;27:1-12. 10.1017/S1431927620024695 Stan T, Thompson ZT, Voorhees PW. Optimizing convolutional neural networks to perform semantic segmentation on digitized histological images of microstructures. J Intell Manuf. 2020;31:595-610. 10.1007/s10845-018-1456-4 Furat O, Petrich L, Finegan DP, Schmidt V. Fusing feature engineering and deep learning: A case study for volumetric segmentation of high-resolution porosimetry images. J Mater Sci. 2022;57:1402-27. 10.1007/s10853-021-06710-7 Ling J, Hutchinson B, Lumley R, Jimenez-Melero E. Microstructure classification of steels using deep convolutional neural networks: Addressing data variability and explainability. Mater Charact. 2021;177:111173. 10.1016/j.matchar.2021.111173 DeCost BL, Lei B, Francis T, Holm EA. High-throughput feature extraction for measuring attributes of deformed microstructure images. Comput Mater Sci. 2020;172:109308. 10.1016/j.commatsci.2019.109308 Ma B, Ban X, Huang H, Liu Y, Yang C. Deep learning-based image classification for microstructure characterization of complex metal alloys. Mater Des. 2020;185:108199. 10.1016/j.matdes.2019.108199 Chen C, Zuo Y, Ye W, Li X, Ong SP. Learning properties of ordered and disordered materials from multi-fidelity data. Nat Comput Sci. 2021;1:46-53. 10.1038/s43588-020-00002-x Jacobs R, Liu T, Wang H, Markl M, Dumpala S, Morgan D. Microstructural feature extraction by deep learning for oligocrystalline materials during tensile testing. J Mater Res. 2021;36:658-70. 10.1557/s43578-020-00027-3 Chen Z, Zhang J, Arjovsky M, Bottou L. Symmetries and invariances in representation learning for materials science. J Chem Phys. 2022;156:124503. 10.1063/5.0083418 Ovadia O, Kahana A, Ziskind D, Yaari E, Sternlieb T, Bucher M, et al. Interpretable deep learning model for microstructural defect detection in TSS 2205 duplex stainless steel welds. J Mater Sci. 2024;59:3832-50. 10.1007/s10853-024-09425-6 Venugopal A, Kumar S, Hamedani HA, Agarwal A. Deep learning for microstructure segmentation and analysis in additive manufactured Ti-6Al-4V. Addit Manuf. 2022;49:102479. 10.1016/j.addma.2021.102479 Shen C, Wang C, Huang X, Roberts N, Zhang Z. Deep learning assisted phase retrieval for X-ray ptychography of microstructures. Opt Express. 2020;28:17376-91. 10.1364/OE.393221 Chan H, Cherukara MJ, Narayanan B, Loeffler TD, Sankaranarayanan SKRS, Cherukara MJ. Machine learning enabled autonomous microstructural characterization in 3D samples. npj Comput Mater. 2022;8:55. 10.1038/s41524-022-00736-4 Damewood J, Ward L, Blaiszik B. Deep language models for interpretative and predictive materials science. APL Mach Learn. 2023;1:010901. 10.1063/5.0133585 Hsu Y-C, Yu C-H, Buehler MJ. What can machine learning help with microstructure-informed materials modeling and design? MRS Bull. 2024;49:1036-46. 10.1557/s43577-024-00797-4 Tang M, Zou Y. Interpretable machine learning approach for predicting process-structure-property relationships in metal additive manufacturing. Addit Manuf. 2024;87:104263. 10.1016/j.addma.2024.104263 Hasson M, Andersen UL, Winther O, Alstrøm BL, Castelli IE, Hummel F, et al. Compact representations of microstructure images using triplet networks. npj Comput Mater. 2020;6:157. 10.1038/s41524-020-00423-2 Zheng X, Watanabe I, Paik J, Li J, Guo X, Naito M. Text-to-microstructure generation using generative deep learning. Small. 2024;20:2402685. 10.1002/smll.202402685 Wan J, Wang H, Ding F, Zhang S, Xu B, Wang D, et al. Advancing materials science through next-generation machine learning. Mater Today. 2024;74:123-39. 10.1016/j.mattod.2024.02.002