iamrp.net-E3Ff-1788861347-v100612202 1788861347 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 2022 1 2 Daniel Fischer Laura Meier Thomas Braun 2022 10.68159/v100612202 https://iamrp.net/pub/journal/1/article/v100612202 Li K, Rubungo AN, Lei X, Persaud D, Choudhary K, DeCost B, et al. Probing out-of-distribution generalization in machine learning for materials. Commun Mater. 2025;6(1):9. 10.1038/s43246-024-00731-w Shi X, Zhou L, Huang Y, Wu Y, Hong Z. A review on the applications of graph neural networks in materials science at the atomic scale. Mater Genome Eng Adv. 2024;2(2):e50. 10.1002/mgea.50 Peivaste I, Belouettar S, Mercuri F, Fantuzzi N, Dehghani H, Ghadiri M, et al. Artificial intelligence in materials science and engineering: Current landscape, key challenges, and future trajectories. Compos Struct. 2025;372(C):119419. 10.1016/j.compstruct.2025.119419 Shimakawa H, Kumada A, Sato M. Extrapolative prediction of small-data molecular property using quantum mechanics-assisted machine learning. Npj Comput Mater. 2024;10(1):11. 10.1038/s41524-023-01194-2 Zhang L, Banko L, Schuhmann W, Ludwig A, Stricker M. Composition-property extrapolation for compositionally complex solid solutions based on word embeddings. Digit Discov. 2025;4(1):1578-90. 10.1039/D5DD00169B Li Q, Fu N, Omee SS, Hu J. Md-hit: Machine learning for material property prediction with dataset redundancy control. Npj Comput Mater. 2024;10(1):245. 10.1038/s41524-024-01426-z Oliynyk A, Lee S, Jaffal E, Kumar NB, Kleinke H, Shiryaev D, et al. Composition and structure analyzer/featurizer for explainable machine-learning models to predict solid state structures. Digit Discov. 2025;4(4):548-60. 10.1039/D4DD00332B Yang K, Song Y, Li Y, Smedskjaer MM, Bauchy M, Rosner F. Enabling extrapolation of young’s modulus of cao-al2o3-sio2 ternary glasses by topology-informed machine learning. J Non Cryst Solids. 2025;666(C):123610. 10.1016/j.jnoncrysol.2025.123610 Gao L, Lin J, Wang L, Du L. Machine learning-assisted design of advanced polymeric materials. Acc Mater Res. 2024;5(5):571-84. 10.1021/accountsmr.3c00288 Wang Y, Li S, Li S, Li S, Chen M. Prediction, interpretation and extrapolation for shear modulus and bulk modulus of solid-state electrolytes based on machine learning. Mater Today Commun. 2024;38:108294. 10.1016/j.mtcomm.2024.108294 Cao Y, Taghvaie Nakhjiri A, Ghadiri M, Ghadiri M. Different applications of machine learning approaches in materials science and engineering: Comprehensive review. Eng Appl Artif Intell. 2024;135(C):108783. 10.1016/j.engappai.2024.108783 Hassan M, Fateh AA, Lin J, Zhuang Y, Lin G, Xiong H, et al. Unfolding explainable ai for brain tumor segmentation. Neurocomputing. 2024;599:128058. 10.1016/j.neucom.2024.128058 Alosious S, Jiang M, Luo T. Computation and machine learning for materials: Past, present, and future perspectives. MRS Bull. 2025;50(10):1212-24. 10.1007/s43577-025-00959-y