iamrp.net-oFLD-1792514633-j356723370 1792514633 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 2025 4 2 Anna Svensson Erik Lindberg 2025 10.68159/j356723370 https://iamrp.net/pub/journal/1/article/j356723370 Chitturi SR, Ramdas A, Wu Y, Rohr B, Ermon S, Dionne J, et al. Targeted materials discovery using Bayesian algorithm execution. npj Comput Mater. 2024;10(1):156. Kumar VV, Santos TF, Santos CM, Nzeyimana BS, Shahin K, Jeevan Rao H, et al. Artificial intelligence and machine learning approaches for composite materials: Recent developments and applications. Polym Compos. 2026. 10.1002/pc.71029 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. Montoya JH, Aykol M, Anapolsky A, Gopal CB, Herring PK, Hummelshøj JS, et al. Toward autonomous materials research: Recent progress and future challenges. Appl Phys Rev. 2022;9(1). 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. Banik S, Loeffler TD, Batra R, Singh H, Cherukara MJ, Sankaranarayanan SK. Learning with delayed rewards: A case study on inverse defect design in 2D materials. ACS Appl Mater Interfaces. 2021;13(30):36455-64. Loewenstein G, Carbone E. Self-control≠ temporal discounting. Curr Opin Psychol. 2024;60:101924. Han B, Ren Z, Wu Z, Zhou Y, Peng J. Off-policy reinforcement learning with delayed rewards. In: International Conference on Machine Learning. PMLR; 2022. p. 8280-303. Arjona-Medina JA, Gillhofer M, Widrich M, Unterthiner T, Brandstetter J, Hochreiter S. Rudder: Return decomposition for delayed rewards. Adv Neural Inf Process Syst. 2019;32. Croote DE, Lai B, Hu J, Baxter MG, Montagrin A, Schiller D. Delay discounting decisions are linked to temporal distance representations of world events across cultures. Sci Rep. 2020;10(1):12913. Barnett A. I’m going to halve my publication output: You should consider slow science, too. Nature. 2026. Stengers I, Muecke S. Another science is possible. Cambridge (UK): Polity; 2018. Erickson KI, Donofry SD, Sewell KR, Brown BM, Stillman CM. Cognitive aging and the promise of physical activity. Annu Rev Clin Psychol. 2022;18(1):417-42. Kikkawa N, Ohno H. Materials discovery using max K-armed bandit. J Mach Learn Res. 2024;25(100):1-40. Zhang S, Sridharan M. A survey of knowledge-based sequential decision-making under uncertainty. AI Mag. 2022;43(2):249-66. Xu P. Efficient and robust sequential decision making algorithms. AI Mag. 2024;45(3):376-85. Wan R, Liu Y, McQueen J, Hains D, Song R. Experimentation platforms meet reinforcement learning: Bayesian sequential decision-making for continuous monitoring. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining; 2023. p. 5016-27. Hübotter J, Sukhija B, Treven L, As Y, Krause A. Transductive active learning with application to safe Bayesian optimization. In: ICML 2024 Workshop: Aligning Reinforcement Learning Experimentalists and Theorists; 2024. Gabrielli M, Antonelli M, Trovò F. Adapting bandit algorithms for settings with sequentially available arms. Eng Appl Artif Intell. 2024;131:107815. Yaghoubi AA, Gandomzadeh M, Gholami A, Gavagsaz-Ghoachani R, Zandi M. Long-term comparative analysis of machine learning models: A deep dive into applications of artificial intelligence for enhancing photovoltaic performance prediction. Int J Electr Power Energy Syst. 2025;170:110866. Salas M, Singh A, Pignataro C, Pal L. AI-powered open-source infrastructure for accelerating materials discovery and advanced manufacturing. Commun Mater. 2026;7(1):65. Gupta KK, Barman S, Sankar A, Dey S, Mukhopadhyay T. Artificial intelligence in materials by design: Critical review and perspectives on materials informatics to generative and agentic intelligence. Arch Comput Methods Eng. 2026:1-31. Westermayr J, Marquetand P. Machine learning spectroscopy to advance computation and analysis. Chem Sci. 2025;16(46):21660-76. Vergadou N, Constantoudis V. Machine learning approaches in soft matter molecular simulation and materials characterization: Challenges and perspectives. ChemPlusChem. 2025;90(12):e202500267. Zeng B, Wu S, Yao S. Machine learning-driven paradigm for polymer aging lifetime prediction: Integrating multi-mechanism coupling and cross-scale modeling. Polymers. 2025;17(22):2991. Wellmann PJ. The search for new materials and the role of novel processing routes. Discov Mater. 2021;1(1):14. Krichen M, Mihoub A. Long short-term memory networks: A comprehensive survey. AI. 2025;6(9):215.