iamrp.net-RAIt-1791515267-n838736242 1791515267 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 Oliver Grant Daniel Brooks Amelia Carter 2025 10.68159/n838736242 https://iamrp.net/pub/journal/2/article/n838736242 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. Szymanski NJ, Rendy B, Fei Y, Kumar RE, He T, Milsted D, et al. An autonomous laboratory for the accelerated synthesis of inorganic materials. Nature. 2023;624(7990):86-91. 10.1038/s41586-023-06734-w MacLeod BP, Parlane FGL, Morris RH, Berlinguette CP. Self-driving laboratory for accelerated discovery of thin-film materials. Sci Adv. 2020;6(20):eaaz8867. Boiko DA, MacKnight R, Kline B, Gomes G. Autonomous chemical research with large language models. Nature. 2023;624(7992):570-8. 10.1038/s41586-023-06792-0 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. Hall BL, Taylor CJ, Labes R, Massey AF, Menzel R, Bourne RA, et al. Autonomous optimisation of a nanoparticle catalysed reduction reaction in continuous flow. Chem Commun. 2021;57(40):4926-9. 10.1039/D1CC00859E Pardakhti M, Moharreri E, Wanik D, Suib SL, Srivastava R. Machine learning using combined structural and chemical descriptors for prediction of methane adsorption performance of metal organic frameworks (mofs). ACS Comb Sci. 2017;19(10):640-5. 10.1021/acscombsci.7b00056 Shi Z, Yang W, Deng X, Cai C, Yan Y, Liang H, et al. Machine-Learning-Assisted high-throughput computational screening of high performance metal-organic frameworks. Mol Syst Des Eng. 2020;5(4):725-42. 10.1039/D0ME00005A Tran K, Ulissi ZW. Active learning across intermetallics to guide discovery of electrocatalysts for co2 reduction and h2 evolution. Nat Catal. 2018;1(9):696-703. Zhang X, Xu Z, Wang Z, Liu H, Zhao Y, Jiang S. High-Throughput and machine learning approaches for the discovery of metal organic frameworks. APL Mater. 2023;11:060901. 10.1063/5.0147650 Borboudakis G, Stergiannakos T, Frysali M, Klontzas E, Tsamardinos I, Froudakis GE. Chemically intuited, large-scale screening of mofs by machine learning techniques. npj Comput Mater. 2017;3:40. 10.1038/s41524-017-0045-8 Bucior BJ, Bobbitt NS, Islamoglu T, Goswami S, Gopalan A, Yildirim T, et al. Energy-Based descriptors to rapidly predict hydrogen storage in metal-organic frameworks. Mol Syst Des Eng. 2019;4(1):162-74. 10.1039/C8ME00050F Altintas C, Altundal OF, Keskin S, Yildirim R. Machine learning meets with metal organic frameworks for gas storage and separation. J Chem Inf Model. 2021;61(5):2131-46. 10.1021/acs.jcim.1c00191 Moliner M, Román-Leshkov Y, Corma A. Machine learning applied to zeolite synthesis: The missing link for realizing high-throughput discovery. Acc Chem Res. 2019;52(10):2971-80. Häse F, Roch LM, Kreisbeck C, Aspuru-Guzik A. Phoenics: A bayesian optimizer for chemistry. ACS Cent Sci. 2018;4(9):1134-45. 10.1021/acscentsci.8b00307 Dave A, Mitchell J, Burke S, Lin H, Whitacre J, Viswanathan V. Autonomous optimization of non-aqueous li-ion battery electrolytes via robotic experimentation and machine learning coupling. Nat Commun. 2022;13(1):5454. 10.1038/s41467-022-32938-1 Scheffler M, Aeschlimann M, Albrecht M, Bereau T, Bungartz HJ, Felser C, et al. FAIR data enabling new horizons for materials research. Nature. 2022;604(7907):635-42. 10.1038/s41586-022-04501-x Hung L, Yager JA, Monteverde D, Baiocchi D, Kwon H-K, Sun S, et al. Autonomous laboratories for accelerated materials discovery: A community survey and practical insights. Digit Discov. 2024;3(8):1273-9. Greenaway RL, Jelfs KE. Integrating computational and experimental workflows for accelerated organic materials discovery. Adv Mater. 2021;33(11):2004831. 10.1002/adma.202004831 Eyke NS, Green WH, Jensen KF. Iterative experimental design based on active machine learning reduces the experimental burden associated with reaction screening. React Chem Eng. 2020;5(10):1963-72. 10.1039/D0RE00232A Dunlap JH, Ethier JG, Putnam-Neeb AA, Iyer S, Luo S-X L, Feng H, et al. Continuous flow synthesis of pyridinium salts accelerated by multi-objective bayesian optimization with active learning. Chem Sci. 2023;14(30):8061-9. 10.1039/D3SC01303K Jorayev P, Russo D, Tibbetts JD, Schweidtmann AM, Deutsch P, Bull SD, et al. Multi-objective bayesian optimisation of a two-step synthesis of p-cymene from crude sulphate turpentine. Chem Eng Sci. 2022;247:116938. 10.1016/j.ces.2021.116938 Kondo M, Sugizaki A, Khalid MI, Wathsala HDP, Ishikawa K, Hara S, et al. Energy-, time-, and labor-saving synthesis of α-ketiminophosphonates: Machine-Learning-Assisted simultaneous multiparameter screening for electrochemical oxidation. Green Chem. 2021;23(16):5825-31. 10.1039/D1GC01583D Naito Y, Kondo M, Nakamura Y, Shida N, Ishikawa K, Washio T, et al. Bayesian optimization with constraint on passed charge for multiparameter screening of electrochemical reductive carboxylation in a flow microreactor. Chem Commun. 2022;58(24):3893-6. 10.1039/D2CC00124A Kondo M, Wathsala HDP, Sako M, Hanatani Y, Ishikawa K, Hara S,et al. Exploration of flow reaction conditions using machine-learning for enantioselective organocatalyzed rauhut-currier and [3+2] annulation sequence. Chem Commun. 2020;56(8):1259-62. 10.1039/C9CC08526B Kim E, Huang K, Jegelka S, Olivetti E. Virtual screening of inorganic materials synthesis parameters with deep learning. npj Comput Mater. 2017;3:53. 10.1038/s41524-017-0055-6 Choudhary K, DeCost B, Chen C, Jain A, Tavazza F, Cohn R, et al. Recent advances and applications of deep learning methods in materials science. npj Comput Mater. 2022;8(1):59. 10.1038/s41524-022-00734-6 Himanen L, Geurts A, Foster AS, Rinke P. Data-Driven materials science: Status, challenges, and perspectives. Adv Sci. 2019;6(21):1900808. 10.1002/advs.201900808 Kalinin SV, Ziatdinov M, Hinkle J, Jesse S, Ghosh A, Kelley KP, et al. Automated and autonomous experiments in electron and scanning probe microscopy. ACS Nano. 2021;15(8):12653-78. Xu P, Ji X, Li M, Lu W. Small data machine learning in materials science. npj Comput Mater. 2023;9:42. 10.1038/s41524-023-01000-z Meredig B, Antono E, Church C, Hutchinson M, Young J, Paradiso S, et al. Can machine learning identify the next high-temperature superconductor? Identifying and avoiding common pitfalls. npj Comput Mater. 2018;4:21. Wang Y, Wagner N, Rondinelli JM. Symbolic regression in materials science. MRS Commun. 2019;9(3):793-805. 10.1557/mrc.2019.85