iamrp.net-Vlpd-1791533617-m563077752 1791533617 Institute for Advanced Materials Research Press info@iamrp.net Institute for Advanced Materials Research Press Journal of Computational and Data-Driven Materials Engineering J. Comput. Data-Driven Mater. Eng. 3149-9368 2025 4 1 Mohammed Al-Farsi Salim Al-Harthy Nasser Al-Rawahi 2025 10.68159/m563077752 https://iamrp.net/pub/journal/2/article/m563077752 Zuo Y, Qin M, Chen C, Ye W, Li X, Luo J, et al. Accelerating materials discovery with Bayesian optimization and graph deep learning. Mater Today. 2021;51:126-35. 10.1016/j.mattod.2021.08.012 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 Cinquin T, Lo S, Strieth-Kalthoff F, Aspuru-Guzik A, Pleiss G, Bamler R, et al. What actually matters for materials discovery: Pitfalls and recommendations in Bayesian optimization. In: AI for Accelerated Materials Design - ICLR 2025; 2025. Liu Y, Cui Y, Zhou H, Lei S, Yuan H, Shen T, et al. Machine learning-based methods for materials inverse design: A review. Comput Mater Contin. 2025;82(2):1463-92. 10.32604/cmc.2025.060109 Abolhasani M, Kumacheva E. The rise of self-driving labs in chemical and materials sciences. Nat Synth. 2023;2(6):483-92. 10.1038/s44160-022-00231-0 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. 10.1038/s41524-019-0153-8 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. 10.1039/D1ME00154J Priyadarshini MS, Romiluyi O, Wang Y, Miskin K, Ganley C, Clancy P. PAL 2.0: A physics-driven Bayesian optimization framework for material discovery. Mater Horiz. 2024;11(3):781-91. 10.1039/D3MH01474F Chen X, Zhou H, Li Y. Effective design space exploration of gradient nanostructured materials using active learning based surrogate models. Mater Des. 2019;183:108085. 10.1016/j.matdes.2019.108085 Strieth-Kalthoff F, Hao H, Rathore V, Derasp J, Gaudin T, Angello NH, et al. Delocalized, asynchronous, closed-loop discovery of organic laser emitters. Science. 2024;384(6697):eadk9227. 10.1126/science.adk9227 Angello NH, Friday DM, Hwang C, Yi S, Cheng AH, Torres-Flores TC, et al. Closed-loop transfer enables artificial intelligence to yield chemical knowledge. Nature. 2024;633(8029):351-8. 10.1038/s41586-024-07892-1 Lee J, Park D, Lee M, Lee H, Park K, Lee I, et al. Machine learning-based inverse design methods considering data characteristics and design space size in materials design and manufacturing: A review. Mater Horiz. 2023;10(12):5436-56. 10.1039/D3MH00039G Ha CS, Yao D, Xu Z, Liu C, Liu H, Elkins D, et al. Rapid inverse design of metamaterials based on prescribed mechanical behavior through machine learning. Nat Commun. 2023;14(1):5765. 10.1038/s41467-023-40854-1 Herbol HC, Poloczek M, Clancy P. Cost-effective materials discovery: Bayesian optimization across multiple information sources. Mater Horiz. 2020;7(8):2113-23. 10.1039/D0MH00062K Khatamsaz D, Vela B, Singh P, Johnson DD, Allaire D, Arróyave R. Bayesian optimization with active learning of design constraints using an entropy-based approach. NPJ Comput Mater. 2023;9(1):49. 10.1038/s41524-023-01006-7 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. 10.1093/oxfmat/itac006 Park J, Kim YM, Hong S, Han B, Nam KT, Jung Y. Closed-loop optimization of nanoparticle synthesis enabled by robotics and machine learning. Matter. 2023;6(3):677-90. 10.1016/j.matt.2023.01.018 Arróyave R, Khatamsaz D, Vela B, Couperthwaite R, Molkeri A, Singh P, et al. A perspective on Bayesian methods applied to materials discovery and design. MRS Commun. 2022;12(6):1037-49. 10.1557/s43579-022-00288-0 Wang J, Wang Y, Chen Y. Inverse design of materials by machine learning. Materials (Basel). 2022;15(5):1811. 10.3390/ma15051811 Bassman Oftelie L, Rajak P, Kalia RK, Nakano A, Sha F, Sun J, et al. Active learning for accelerated design of layered materials. NPJ Comput Mater. 2018;4(1):74. 10.1038/s41524-018-0129-0 Abu-Mualla M, Huang J. Inverse design of 3D cellular materials with physics-guided machine learning. Mater Des. 2023;232:112103. 10.1016/j.matdes.2023.112103 Liu Z, Zhu D, Raju L, Cai W. Tackling photonic inverse design with machine learning. Adv Sci (Weinh). 2021;8(5):2002923. 10.1002/advs.202002923 Chen CT, Gu GX. Generative deep neural networks for inverse materials design using backpropagation and active learning. Adv Sci (Weinh). 2020;7(5):1902607. 10.1002/advs.201902607 Seifrid M, Pollice R, Aguilar-Granda A, Chan ZM, Hotta K, Ser CT, et al. Autonomous chemical experiments: Challenges and perspectives on establishing a self-driving lab. Acc Chem Res. 2022;55(17):2454-66. 10.1021/acs.accounts.2c00220 Baird SG, Sparks TD. What is a minimal working example for a self-driving laboratory? Matter. 2022;5(12):4170-8. 10.1016/j.matt.2022.11.007 Soldatov MA, Butova VV, Pashkov D, Butakova MA, Medvedev PV, Chernov AV, et al. Self-driving laboratories for development of new functional materials and optimizing known reactions. Nanomaterials (Basel). 2021;11(3):619. 10.3390/nano11030619 Zaki M, Prinz C, Ruehle B. A self-driving lab for nano- and advanced materials synthesis. ACS Nano. 2025;19(9):9029-41. 10.1021/acsnano.4c17504 Tamura R, Tsuda K, Matsuda S. NIMS-OS: An automation software to implement a closed loop between artificial intelligence and robotic experiments in materials science. Sci Technol Adv Mater Methods. 2023;3(1):2232297. 10.1080/27660400.2023.2232297 Lo S, Baird SG, Schrier J, Blaiszik B, Carson N, Foster I, et al. Review of low-cost self-driving laboratories in chemistry and materials science: The “frugal twin” concept. Digit Discov. 2024;3(5):842-68. 10.1039/D3DD00223C