iamrp.net-zOFI-1788862144-p251238849 1788862144 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 Tanaka Yuki Sato Kenji Mori Rina Okabe 2026 10.68159/p251238849 https://iamrp.net/pub/journal/1/article/p251238849 Chen C, Ong SP. Uncertainty quantification for machine learning interatomic potentials in materials. npj Comput Mater. 2024;10:83. Merchant A, Batzner S, Schoenholz SS, Aykol M, Cheon G, Cubuk ED. Scaling deep learning for materials discovery. Nature. 2023;624(7990):80-5. 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. Zhong M, Tran K, Min Y, et al. Accelerating materials discovery with bayesian optimization. Nat Rev Mater. 2022;7:358-70. Chen C, Ong SP. Direct error modeling for improved uncertainty quantification in regression tasks: Application to battery materials. Joule. 2022;6:1234-51. Kumagai Y, Ando K, Tanaka I. Effects of data bias on machine-learning-based material discovery using experimental property data. Sci Technol Adv Mater. 2022;23:1-12. Henschen T. How strong is the argument from inductive risk? Eur J Philos Sci. 2021;11(3):92. Butler KT, Davies DW, Cartwright H, et al. Materials informatics. J Phys Condens Matter. 2021;33:203001. Park CW, Wolverton C. Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery. Phys Rev Mater. 2020;4(6):063801. Heid E, Green WH. Characterizing uncertainty in machine learning for chemistry. J Chem Inf Model. 2023;63:4012-25. Rao Z, Bajpai A, Zhang H. Active learning strategies for the design of sustainable alloys. Philos Trans A. 2024;382(2284):20230242. Yang Y, Jiménez-Negrón OA, Kitchin JR. Machine-learning accelerated geometry optimization in molecular simulation. J Chem Phys. 2021;154(23). Oviedo F, Ferres JL, Buonassisi T, Butler KT. Interpretable and explainable machine learning for materials science and chemistry. Acc Mater Res. 2022;3:597-607. Rudner R, Toner J. Values and inductive risk in machine learning modelling: The case of binary classification models. Eur J Philos Sci. 2021;11:78. Lam H, Rescorla M. On predicting recidivism: Epistemic risk, tradeoffs, and values in machine learning. Can J Philos. 2022;52:321-41. Morgan JN. Calibration after bootstrap for accurate uncertainty quantification in regression models. npj Comput Mater. 2022;8:112. Peerless JS, et al. Uncertainty quantification in battery materials discovery. J Phys Chem C. 2020;124:14567-78. Mosavi A, et al. Materials informatics and sustainability. Data Centric Eng. 2021;2:e12. Wang X, Zhao Y, Pourpanah F. Recent advances in deep learning. Int J Mach Learn Cybern. 2020;11(4):747-50. Agrawal A, Choudhary A. Perspective: Materials informatics and big data. APL Mater. 2020;8:080401. Hutchinson ML, Antono E, Gibbons BM, Paradiso S, Ling J, Meredig B. Overcoming data scarcity in materials science with transfer learning. Comput Mater Sci. 2021;186:110012. Childs CM, Washburn NR. Integrating domain knowledge into machine learning for materials. MRS Bull. 2021;46:765-71. Mosavi A, et al. State of the art survey of deep learning for uncertainty quantification in materials. Eng Appl Artif Intell. 2022;112:104845. Oviedo F, et al. Explainable machine learning in materials science. npj Comput Mater. 2022;8:204. Hüllermeier E, Waegeman W. Aleatoric and epistemic uncertainty in machine learning. Mach Learn. 2021;110:457-506. Choudhary K, et al. Materials property prediction with uncertainty quantification: A benchmark study. Appl Phys Rev. 2023;10:021409. Wen M, Tadmor EB. Uncertainty quantification for neural network potential foundation models. npj Comput Mater. 2020;6:175. Liu Z, Singh A, Li Y. Feature importance and uncertainty quantification of machine learning model in materials science. Ind Eng Chem Res. 2023;62:14567-78. Furat O, et al. Uncertainty quantification using generative adversarial networks. npj Comput Mater. 2022;8:68. Yin P, Panapitiya G. Evaluating uncertainty-based active learning for accelerating the generalization of molecular property prediction. J Chem Inf Model. 2023;63:5123-34. Pyzer-Knapp EO, Pitera JW, Staar PW, Takeda S, Laino T, Sanders DP, et al. Accelerating materials discovery using artificial intelligence, high performance computing and robotics. npj Comput Mater. 2022;8(1):84. Honarmandi P, Arróyave R. Uncertainty quantification and propagation in computational materials science and simulation-assisted materials design. Integr Mater Manuf Innov. 2020;9(1):103-43. Liu L, Zhou W, Guan K, Peng B, Xu S, Tang J, et al. Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems. Nat Commun. 2024;15(1):357. Morgan D, Jacobs R. Opportunities and challenges for machine learning in materials science. Annu Rev Mater Res. 2020;50:71-103. Lookman T, et al. Benchmarking active learning strategies for materials optimization and discovery. Oxf Open Mater Sci. 2022;2:itac006.