Scientific Blind Spots Introduced by Feature Engineering in Materials Informatics

iamrp.net-RzwN-1793858494-o637133842 1793858494 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 2023 2 1 Scientific Blind Spots Introduced by Feature Engineering in Materials Informatics Fatima Zahra Amrani Youssef Benali Samira El-Haddad 2023 10.68159/o637133842 https://iamrp.net/pub/journal/1/article/o637133842 Wang Y, Zhang Y, Chen C. Small data machine learning in materials science. npj Comput Mater. 2023;9(1):42. 10.1038/s41524-023-01000-z 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. 10.1038/s41524-024-01331-5 Kailkhura B, Gallagher B, Kim S, Hiszpanski A. Explainable machine learning in materials science. npj Comput Mater. 2022;8(1):204. 10.1038/s41524-022-00884-7 Wang Z, Chen A, Tao K, Cai J, Gao J, Ye S, et al. AlphaMat: a material informatics hub connecting data, features, models and applications. npj Comput Mater. 2023;9(1):130. 10.1038/s41524-023-01086-5 Himanen L, Geurts A, Foster AS, Rinke P. Data-driven materials science: status, challenges, and perspectives. Adv Sci. 2020;7(20):1900808. 10.1002/advs.201900808 Morgan D, Jacobs R. Opportunities and challenges for machine learning in materials science. Annu Rev Mater Res. 2020;50:71-103. 10.1146/annurev-matsci-070218-010015 Pankajakshan P, Sanyal S, de Albuquerque Oliveira O, Blaiszik B, Foster I, Assary RS, et al. Machine learning for materials scientists: An introductory guide toward best practices. Chem Mater. 2020;32(10):4360-72. 10.1021/acs.chemmater.0c01907 Chen C, Zuo Y, Ye W, Li X, Deng Z, Ong SP. A critical review of machine learning of energy materials. Adv Energy Mater. 2020;10(31):1903242. 10.1002/aenm.201903242 Zhang Y, Xu L, Berlinguette CP. Feature selection for machine learning-driven accelerated discovery and optimization in emerging photovoltaics: A review. Adv Intell Discov. 2025;2:e202500022. 10.1002/aidi.202500022 Jacobs R, Mayeshiba T, Afflerbach B, Miles L, Williams M, Turner M, et al. The materials simulation toolkit for machine learning (MAST-ML): An automated open source toolkit to accelerate data-driven materials research. Comput Mater Sci. 2020;176:109544. 10.1016/j.commatsci.2020.109544 Oviedo F, Ferres JL, Buonassisi T, Butler KT. Interpretable and explainable machine learning for materials science and chemistry. Acc Mater Res. 2022;3(6):597-607. 10.1021/accountsmr.1c00244 Zhu L, Zhou J, Sun Z. Materials data toward machine learning: advances and challenges. J Phys Chem Lett. 2022;13(18):3965-77. 10.1021/acs.jpclett.2c00576 Sendek AD, Cheon G, Pasta M, Reed EJ. Quantifying the search space for high-throughput Li-ion conductor discovery. Chem Mater. 2020;32(1):253-61. 10.1021/acs.chemmater.9b04078 Li K, DeCost B, Choudhary K, Greenwood M, Hattrick-Simpers J. A critical examination of robustness and generalizability of machine learning prediction of materials properties. npj Comput Mater. 2023;9(1):55. 10.1038/s41524-023-01012-9 Ma B, Wu X, Zhao C, Lin C, Gao M, Sa B, et al. An interpretable machine learning strategy for pursuing high piezoelectric coefficient in Pb-free ceramics. npj Comput Mater. 2023;9:1-12. 10.1038/s41524-023-01187-1 Liu X, Xu P, Zhao J, Lu W, Li M, Wang G. Material machine learning for alloys: applications, challenges and perspectives. J Mater Sci Technol. 2023;95:1-18. Stuart S, Watchorn J, Gu F. Sizing up feature descriptors for macromolecular machine learning with polymeric biomaterials. npj Comput Mater. 2023;9:1-12. 10.1038/s41524-023-01040-5 Owens CB, Mathew N, Olaveson TW, Tavenner JP, Kober E, Tucker GJ, et al. Feature engineering descriptors, transforms, and machine learning for grain boundaries and variable-sized atom clusters. npj Comput Mater. 2024;10. 10.1038/s41524-024-01509-x Schmidt J, Marques MRG, Botti S, Marques MAL. Recent advances and applications of machine learning in solid-state materials science. npj Comput Mater. 2019;83:5. Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A. Machine learning for molecular and materials science. Nature. 2018;559(7715):547-55. 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). Chen C, Ye W, Zuo Y, Zheng C, Ong SP. Graph networks as a universal machine learning framework for molecules and crystals. Chem Mater. 2019;31(9). Batra R, Pilania G, Uberuaga BP, Ramprasad R. Multifidelity information fusion with machine learning: a case study of dopant formation energies in hafnia. ACS Appl Mater Interfaces. 2019;11:24906-18. Wen C, Zhang Y, Wang C, et al. Machine learning assisted design of high entropy alloys with desired property. Acta Mater. 2019;170:109-17.