iamrp.net-DuNx-1793850245-l111271176 1793850245 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 Nguyen Van Nam Tran Thi Hoa Le Minh Duc 2026 10.68159/l111271176 https://iamrp.net/pub/journal/1/article/l111271176 Noack MM, Doerk GS, Li R, Streit JK, Vaia RA, Yager KG, et al. Autonomous materials discovery driven by Gaussian process regression with inhomogeneous measurement noise and anisotropic kernels. Sci Rep. 2020;10(1):17663. Sutton R, Barto A. Reinforcement learning: An introduction. Cambridge (MA): MIT Press; 2018. p. 31-329. Plappert M, Houthooft R, Dhariwal P, Sidor S, Chen RY, Chen X, et al. Parameter space noise for exploration. arXiv preprint arXiv:1706.01905. 2017 Jun 6. 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. Zunger A. Inverse design in search of materials with target functionalities. Nat Rev Chem. 2018;2(4):0121. Gómez-Bombarelli R, Wei JN, Duvenaud D, Hernández-Lobato JM, Sánchez-Lengeling B, Sheberla D, et al. Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent Sci. 2018;4(2):268-76. 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(1):21. Xu P, Ji X, Li M, Lu W. Small data machine learning in materials science. npj Comput Mater. 2023;9(1):42. Cesar de Azevedo L, Pinheiro GA, Quiles MG, Da Silva JL, Prati RC. Systematic investigation of error distribution in machine learning algorithms applied to the quantum-chemistry QM9 data set using the bias and variance decomposition. J Chem Inf Model. 2021;61(9):4210-23. Thomas-Mitchell A, Hawe G, Popelier PL. Calibration of uncertainty in the active learning of machine learning force fields. Mach Learn Sci Technol. 2023;4(4):045034. Lookman T, Liu Y, Gao Z. Materials informatics: Emergence to autonomous discovery in the age of AI. Adv Mater. 2026:e15941. Dolar T, della Ventura NM, Mignerot F, Wang Z, Howard HC, Kassner CT, et al. Accelerating materials discovery in heterogeneous composition-property design spaces via collaborative Bayesian optimization. Mater Des. 2025:115331. Xu L, Hoffman N, Wang Z, Xu H. Harnessing structural stochasticity in the computational discovery and design of microstructures. Mater Des. 2022;223:111223. Jiang D, Cao C, Zhang J, Zhang O, Zhu C, Xu J, et al. Stochasticity-by-design in memristive materials for probabilistic computing and hardware security: Entropy sources, control knobs, and benchmarks. ACS Appl Electron Mater. 2026. Wang A, Liang H, McDannald A, Takeuchi I, Kusne AG. Benchmarking active learning strategies for materials optimization and discovery. Oxf Open Mater Sci. 2022;2(1):itac006. Stach E, DeCost B, Kusne AG, Hattrick-Simpers J, Brown KA, Reyes KG, et al. Autonomous experimentation systems for materials development: A community perspective. Matter. 2021;4(9):2702-26. Diwale S, Eisner MK, Carpenter C, Sun W, Rutledge GC, Braatz RD. Bayesian optimization for material discovery processes with noise. Mol Syst Des Eng. 2022;7(6):622-36. Wei Y, Peng B, Xie R, Chen Y, Qin Y, Wen P, et al. Deep active optimization for complex systems. Nat Comput Sci. 2025;5(9):801-12. Pan X, Xie Y, Li C, He Y, Zhang Y, Wang Y, et al. Convergence of computational materials science and AI for next-generation energy storage materials. J Electron Mater. 2026;55(1):45-114. Raihan AS, Khosravi H, Das S, Ahmed I. Accelerating material discovery with a threshold-driven hybrid acquisition policy-based Bayesian optimization. Manuf Lett. 2024;41:1300-11. 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. Johnson NS, Vulimiri PS, To AC, Zhang X, Brice CA, Kappes BB, et al. Invited review: Machine learning for materials developments in metals additive manufacturing. Addit Manuf. 2020;36:101641. Bi J, Xu Y, Conrad F, Wiemer H, Ihlenfeldt S. A comprehensive benchmark of active learning strategies with AutoML for small-sample regression in materials science. Sci Rep. 2025;15(1):37167. Ma Y, Gao Y, Wang L, Chen M, Cui W, Wang B, et al. Accelerating materials discovery through active learning: Methods, challenges and opportunities. Innov Info. 2025;1(1):100013. Li Y, Jiang E, Ni Z, Li W, Huang M, Zhao F, et al. A study of the role of data and model uncertainty in active learning. Comput Mater Sci. 2025;247:113512. Patel KP, Maicke A, Arzate J, Kwon J, Smith JD, Aimone JB, et al. AI-guided framework for the design of materials and devices for magnetic-tunnel-junction-based true random number generators. Commun Eng. 2025;4(1):43. Chen Y, Deierling P, Xiao S. Exploring active learning strategies for predictive models in mechanics of materials. Appl Phys A. 2024;130(8):588. Eldabah NM, Pratap A, Pandey A, Sardana N, Sidhu SS, Gepreel MA. Design approaches of high-entropy alloys using artificial intelligence: A review. Adv Eng Mater. 2025;27(12):2402504.