Institutional Oversight Models for AI-Directed Materials Innovation

iamrp.net-NWqY-1791515266-i297643486 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 2025 4 2 Institutional Oversight Models for AI-Directed Materials Innovation Carlos Vega Maria Hernandez 2025 10.68159/i297643486 https://iamrp.net/pub/journal/2/article/i297643486 Schmidt J, Marques MRG, Botti S, Marques MAL. Recent advances and applications of machine learning in solid-state materials science. npj Comput Mater. 2019;5:83. 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;3:54. 10.1038/s41524-017-0056-5 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 Lookman T, Balachandran PV, Xue D, Yuan R. Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design. npj Comput Mater. 2019;5:21. 10.1038/s41524-019-0153-8 Merchant A, Batzner S, Schoenholz SS, Aykol M, Cheon G, Cubuk ED. Scaling deep learning for materials discovery. Nature. 2023;624:80-5. 10.1038/s41586-023-06735-9 Sun T, Wang Z, Feng G. Identifying MOFs for electrochemical energy storage via density functional theory and machine learning. npj Comput Mater. 2025;11(90). 10.1038/s41524-025-01590-w 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 Communs. 2020;11:5966. 10.1038/s41467-020-19597-w Szymanski NJ, Rendy B, Fei Y, Kumar RE, He T, Milsted D, et al. An autonomous laboratory for the accelerated synthesis of novel materials. Nature. 2023;624:86-91. 10.1038/s41586-023-06734-w MacLeod BP, Parlane FGL, Morrissey TD, Häse F, Roch LM, Dettelbach KE, et al. Self-driving laboratory for accelerated discovery of thin-film materials. Sci Adv. 2020;6:eaaz8867. 10.1126/sciadv.aaz8867 Foster M, Sumpter BG, Hattrick-Simpers J, Kusne AG, Kalinin SV. Autonomous experimentation systems for materials development: A community perspective. Matter. 2021;4(9):2702-26. 10.1016/j.matt.2021.06.036 Pyzer-Knapp EO, Manica M, Curioni A. Foundation models for materials discovery – current state and future directions. npj Comput Mater. 2025;11(61). 10.1038/s41524-025-01538-0 Lei G, Docherty R, Cooper SJ. Materials science in the era of large language models: a perspective. Digit Discov. 2024;3:1257-72. 10.1039/D4DD00074A Jablonka KM, Ongari D, Moosavi SM, Smit B. Explainable machine learning in materials science. npj Comput Mater. 2022;8:204. 10.1038/s41524-022-00884-7 Maier G, Hamaekers J, Martilotti DS, Ziebarth B. Predicting properties of oxide glasses using informed neural networks. In Informed Machine Learning . Cham: Springer Nature Switzerland; 2025. p. 161-85. Himanen L, Geurts A, Foster AS, Rinke P. Data‐driven materials science: status, challenges, and perspectives. Adv Sci. 2019;6(21):1900808. Shi J, Albreiki F, Colon YJ, Srivastava S, Whitmer JK. Transfer learning facilitates the prediction of polymer–surface adhesion strength. J Chem Theory Comput. 2023;19(14):4631-40. Shen L, Zhou J, Yang T, Yang M, Feng YP. High-throughput computational discovery and intelligent design of two-dimensional functional materials for various applications. Acc Mater Res. 2022;3(6):572-83. De Breuck PP, Wang HC, Rignanese GM, Botti S, Marques MAL. Generative AI for crystal structures: a review. npj Comput Mater. 2025;11(370). 10.1038/s41524-025-01881-2 Kalinin SV, Mukherjee D, Roccapriore K, Blaiszik BJ, Ghosh A, Ziatdinov MA, et al. Machine learning for automated experimentation in scanning transmission electron microscopy. npj Comput Mater. 2023;9(227). 10.1038/s41524-023-01142-0 Volk AA, Abolhasani M. Performance metrics to unleash the power of self-driving labs in chemistry and materials science. Nat communs. 2024;15(1):1378. Minocha N, Pandey P. self-driving labs for materials sciences. Artificial intelligence in polymer science and nanotechnology: Autonomous labs, polymer design, materials simulation; 2025. p. 69.