iamrp.net-C7O6-1792580715-m774705833 1792580715 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 2024 3 1 Paolo Ricci Marco De Luca Giulia Ferraro Antonio Russo 2024 10.68159/m774705833 https://iamrp.net/pub/journal/1/article/m774705833 De Sousa TC, Melo CD. Sustainable infrastructure, industrial ecology, and eco-innovation: Positive impact on society. In: Industry, innovation and infrastructure. Cham: Springer International Publishing; 2021. p. 1093-102. Huyen C. Designing machine learning systems. Sebastopol: O'Reilly Media; 2022. DeCost BL, Hattrick-Simpers JR, Trautt Z, Kusne AG, Campo E, Green ML. Scientific AI in materials science: A path to a sustainable and scalable paradigm. Mach Learn Sci Technol. 2020;1(3):033001. Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A. Machine learning for molecular and materials science. Nature. 2018;559(7715):547-55. Schmidt J, Marques MR, Botti S, Marques MA. Recent advances and applications of machine learning in solid-state materials science. NPJ Comput Mater. 2019;5(1):83. Stier SP, Kreisbeck C, Ihssen H, Popp MA, Hauch J, Malek K, et al. Materials acceleration platforms (MAPs): Accelerating materials research and development to meet urgent societal challenges. Adv Mater. 2024;36(45):2407791. Carriço PJ. Computational materials screening using machine learning methods [master’s thesis]. Coimbra: Universidade de Coimbra; 2021. Ward L, Dunn A, Faghaninia A, Zimmermann NE, Bajaj S, Wang Q, et al. Matminer: An open source toolkit for materials data mining. Comput Mater Sci. 2018;152:60-9. Zhao XG, Zhou K, Xing B, Zhao R, Luo S, Li T, et al. JAMIP: An artificial-intelligence aided data-driven infrastructure for computational materials informatics. Sci Bull. 2021;66(19):1973-85. Choudhary K, Garrity KF, Reid AC, DeCost B, Biacchi AJ, Hight Walker AR, et al. The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design. NPJ Comput Mater. 2020;6(1):173. Huber SP, Zoupanos S, Uhrin M, Talirz L, Kahle L, Häuselmann R, et al. Aiida 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance. Sci Data. 2020;7(1):300. Wang Z, Chen A, Tao K, Cai J, Han Y, Gao J, et al. Alphamat: A material informatics hub connecting data, features, models and applications. NPJ Comput Mater. 2023;9(1):130. Xu P, Ji X, Li M, Lu W. Small data machine learning in materials science. NPJ Comput Mater. 2023;9(1):42. Sbailò L, Fekete Á, Ghiringhelli LM, Scheffler M. The NOMAD artificial-intelligence toolkit: Turning materials-science data into knowledge and understanding. NPJ Comput Mater. 2022;8(1):250. Hu J, Stefanov S, Song Y, Omee SS, Louis SY, Siriwardane EM, et al. Org: A materials informatics web app platform for materials discovery and survey of state-of-the-art. NPJ Comput Mater. 2022;8(1):65. Hegde VI, Borg CK, Del Rosario Z, Kim Y, Hutchinson M, Antono E, et al. Quantifying uncertainty in high-throughput density functional theory: A comparison of aflow, materials project, and OQMD. Phys Rev Mater. 2023;7(5):053805. Shen J, Griesemer SD, Gopakumar A, Baldassarri B, Saal JE, Aykol M, et al. Reflections on one million compounds in the open quantum materials database (OQMD). J Phys Mater. 2022;5(3):031001. Menon S, Lysogorskiy Y, Knoll AL, Leimeroth N, Poul M, Qamar M, et al. From electrons to phase diagrams with machine learning potentials using pyiron based automated workflows. NPJ Comput Mater. 2024;10(1):261. Aykol M. Materials discovery through artificial intelligence. Bull Am Phys Soc. 2020;65. Chibani S, Coudert FX. Machine learning approaches for the prediction of materials properties. APL Mater. 2020;8(8):080701. Takeda S, Kishimoto A, Hamada L, Nakano D, Smith JR. Foundation model for material science. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2023;37(13):15376-83. Wang C, He S, Luo S, Huan Y, Wu S. Brainunit: Integrating physical units into high-performance ai-driven scientific computing. bioRxiv. 2024:2024-09. 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. Hill J, Mannodi-Kanakkithodi A, Ramprasad R, Meredig B. Materials data infrastructure and materials informatics. In: Computational materials system design. Cham: Springer International Publishing; 2018. p. 193-225. Liu Y, Zhao T, Ju W, Shi S. Materials discovery and design using machine learning. J Mater. 2017;3(3):159-77. Ma Y, Xu P, Li M, Ji X, Zhao W, Lu W. The mastery of details in the workflow of materials machine learning. NPJ Comput Mater. 2024;10(1):141. Lei X, Kim E, Baibakova V, Sun S. Lessons in reproducibility: Insights from NLP studies in materials science. arXiv [Preprint]. 2023:arXiv:2307.15759. Wines D, Gurunathan R, Garrity KF, DeCost B, Biacchi AJ, Tavazza F, et al. Recent progress in the JARVIS infrastructure for next-generation data-driven materials design. Appl Phys Rev. 2023;10(4):041302. Reed PM, Hadjimichael A, Moss RH, Brelsford C, Burleyson CD, Cohen S, et al. Multisector dynamics: Advancing the science of complex adaptive human‐Earth systems. Earths Future. 2022;10(3):e2021EF002621.