Small-Data and Sparse-Regime Learning in Materials AI

iamrp.net-JNW3-1793855569-z932480750 1793855569 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 Small-Data and Sparse-Regime Learning in Materials AI — Methods, Assumptions, and Limits Maria Hernandez Carlos Vega Lucia Torres 2026 10.68159/z932480750 https://iamrp.net/pub/journal/1/article/z932480750 Xu P, Ji X, Li M, Lu W. Small data machine learning in materials science. npj Comput Mater. 2023;9:42. Vanpoucke D. Small data materials design with machine learning: When the average model knows best. J Appl Phys. 2020;128:054901. Jain A. Machine learning in materials research: Developments over the last decade and challenges for the future. Curr Opin Solid State Mater Sci. 2024;71:101118. Chen C, Ong SP. A universal graph deep learning interatomic potential for the periodic table. Nat Comput Sci. 2022;2:718-28. Chen H, Ong SP. AtomSets as a hierarchical transfer learning framework for small and large materials datasets. npj Comput Mater. 2021;7:184. Merchant A, Batzner S, Schoenholz SS, Aykol M, Cheon G, Cubuk ED. Scaling deep learning for materials discovery. Nature. 2023;624(7990):80-5. Unni R, Batra R, Ramprasad R. Advancing materials science through next-generation machine learning. Adv Mater. 2024;36:2401234. Yuan L, Guo H, Li Q, Zhang H, Xu M, Zhang W, et al. Machine-learning-assisted material discovery of pyridine-based polymers for efficient removal of ReO4–. Environ Sci Technol. 2024;58(34):15298-310. Herhausen D, Bernritter SF, Ngai EW, Kumar A, Delen D. Machine learning in marketing: Recent progress and future research directions. J Bus Res. 2024;170:114254. Ji X, Xu P, Li M, Lu W. Data quantity governance for machine learning in materials science. Natl Sci Rev. 2023;10:nwad125. Tran K, Neiswanger W, Yoon J, et al. Strategies for incorporating active learning into high-throughput density functional theory calculations. npj Comput Mater. 2023;9:54. Jha D, Singh V, Ward L, et al. Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data. Nat Commun. 2021;12:6593. Yu Y, Xiong J, Wu X, Qian Q. From small data modeling to large language model screening: A dual-strategy framework for materials intelligent design. Adv Sci. 2024;11:2403548. 10.1002/advs.202403548 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;10:141. Lee S, Asahi R. Transfer learning for materials informatics using crystal graph convolutional neural network. Comput Mater Sci. 2021;190:110314. 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. 10.1038/s41467-020-19597-w Frey N, Soklaski R, Rocca D, et al. Benchmarking active learning strategies for materials optimization and discovery. Oxf Open Mater Sci. 2022;2:itac006. Butler KT, Oviedo F, Famprikis T. Gaussian process regression for materials and molecules. Chem Rev. 2021;121:10073-141. Shahzad K, Mardare AI, Hassel AW. Accelerating materials discovery: Combinatorial synthesis, high-throughput characterization, and computational advances. Sci Technol Adv Mater Methods. 2024;4(1):2292486. Pilania G, Ivady V, Zurek E, et al. Multi-fidelity machine learning models for accurate bandgap predictions of solids. Comput Mater Sci. 2021;188:110203. Oviedo F, Ferres JL, Buonassisi T, Butler KT. Interpretable and explainable machine learning for materials science and chemistry. Acc Mater Res. 2022;3:1023-35. Karniadakis GE, Kevrekidis IG, Lu L, Perdikaris P, Wang S, Yang L. Physics-informed machine learning. Nat Rev Phys. 2021;3:422-40. Doncevic DT, Mitsos A, Guo Y, Li Q, Dietrich F, Dahmen M, et al. A recursively recurrent neural network (r2n2) architecture for learning iterative algorithms. SIAM J Sci Comput. 2024;46(2):A719-43. Azari H, Nazari E, Mohit R, Asadnia A, Maftooh M, Nassiri M, et al. Machine learning algorithms reveal potential miRNAs biomarkers in gastric cancer. Sci Rep. 2023;13(1):6147.