iamrp.net-3F9F-1791515264-d226776771 1791515264 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 Natalia Petrova Elena Stoyanova Ivan Dimitrov 2025 10.68159/d226776771 https://iamrp.net/pub/journal/2/article/d226776771 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;11:5966. 10.1038/s41467-020-19597-w Shields BJ, Stevens J, Li J, Parasram M, Damani F, Alvarado JIM, et al. Bayesian reaction optimization as a tool for chemical synthesis. Nature. 2021;590(7844):89-96. 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. MacLeod BP, Parlane FGL, Morrissey TD, Häse F, Roch LM, Dettelbach KE, et al. Self-driving laboratory for accelerated discovery of thin-film materials. Sci Adv. 2020;6(20):eaaz8867. 10.1126/sciadv.aaz8867 Pogue EA, New A, McElroy K, Le NQ, Pekala MJ, McCue I, et al. Closed-loop superconducting materials discovery. npj Comput Mater. 2023;9:181. 10.1038/s41524-023-01131-3 Nambiar AMK, Breen CP, Hart T, Kulesza T, Jamison TF, Jensen KF. Bayesian optimization of computer-proposed multistep synthetic routes on an automated robotic flow platform. ACS Cent Sci. 2022;8(6):825-36. Sim M, Vakili MG, Strieth-Kalthoff F, Hao H, Hickman RJ, Miret S, et al. ChemOS 2.0: An orchestration architecture for chemical self-driving laboratories. Matter. 2024. 10.1016/j.matt.2024.04.022 Roch LM, Häse F, Kreisbeck C, Tamayo-Mendoza T, Yunker LPE, Hein JE, et al. ChemOS: An orchestration software to democratize autonomous discovery. PLoS One. 2020;15(4):e0229862. Tom G, Schmid SP, Baird SG, Cao Y, Darvish K, Hao H, et al. Self-driving laboratories for chemistry and materials science. Chem Rev. 2024;124(16):9633-732. 10.1021/acs.chemrev.4c00055 Abolhasani M, Kumacheva E. The rise of self-driving labs in chemical and materials sciences. Nat Synth. 2023;2(5):483-92. Seifrid M, Pollice R, Aguilar-Granda A, Morgan Chan Z, 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. Burger B, Maffettone PM, Gusev VV, Aitchison CM, Bai Y, Wang X, et al. A mobile robotic chemist. Nature. 2020;583(7815):237-41. Steiner S, Wolf J, Glatzel S, Andreou A, Granda JM, Keenan G, et al. Organic synthesis in a modular robotic system driven by a chemical programming language. Science. 2019;363(6428):eaav2211. 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(6):842-68. 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. 10.1016/j.matt.2021.06.036 Rahmanian F, Flowers J, Guevarra D, Richter M, Fichtner M, Donnely P, et al. Enabling modular autonomous feedback-loops in materials science through hierarchical experimental laboratory automation and orchestration. Adv Mater Interfaces. 2022;9(21):2101987. Häse F, Aldeghi M, Hickman RJ, Roch LM, Aspuru-Guzik A. GRYFFIN: An algorithm for Bayesian optimization of categorical variables informed by expert knowledge. Appl Phys Rev. 2021;8(3):031406. Aldeghi M, Häse F, Hickman RJ, Tamblyn I, Aspuru-Guzik A. Golem: An algorithm for robust experiment and process optimization. Chem Sci. 2021;12(44):14792-807. Xie Y, Zhang C, Deng H, Zheng B, Su J-W, Shutt K, et al. Accelerate synthesis of metal-organic frameworks by a robotic platform and Bayesian optimization. ACS Appl Mater Interfaces. 2021;13(45):53485-91. Clayton AD, Pyzer-Knapp EO, Purdie M, Jones MF, Barthelme A, Pavey J, et al. Bayesian self-optimization for telescoped continuous flow synthesis. Angew Chem Int Ed. 2023;62(28):e202214511. Adams F, McDannald A, Takeuchi I, Kusne AG. Human-in-the-loop for Bayesian autonomous materials phase mapping. Matter. 2024:7(2):697-709. Caramelli D, Granda JM, Mehr SHM, Cambié D, Henson AB, Cronin L. Discovering new chemistry with an autonomous robotic platform driven by a reactivity-seeking neural network. ACS Cent Sci. 2021;7(11):1821-30. Boiko DA, MacKnight R, Kline B, Gomes G. Autonomous chemical research with large language models. Nature. 2023;624(7992):570-8. Zhu Q, Zhang F, Huang Y, Xiao H, Zhao L, Zhang X, et al. An all-round AI-chemist with a scientific mind. Natl Sci Rev. 2022;9(12):nwac190. Granda JM, Donina L, Dragone V, Long D-L, Cronin L. Controlling an organic synthesis robot with machine learning to search for new reactivity. Nature. 2018;559(7714):377-81. Duros V, Grizou J, Xuan W, Hosni Z, Long D-L, Miras HN, et al. Human versus robots in the discovery and crystallization of gigantic polyoxometalates. Angew Chem Int Ed. 2017;56(36):10815-20. Masubuchi S, Morimoto M, Morikawa S, Onodera M, Asakawa Y, Watanabe K, et al. Autonomous robotic searching and assembly of two-dimensional crystals to build van der Waals superlattices. Nat Commun. 2018;9:1413.