iamrp.net-T9tU-1792546419-w460057148 1792546419 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 Bruno Martins Lucas Pereira 2026 10.68159/w460057148 https://iamrp.net/pub/journal/1/article/w460057148 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 Jablonka KM, Ongari D, Moosavi SM, Smit B. Big-data science in porous materials: Materials genomics and machine learning. Chem Rev. 2020;120(16):8066-129. 10.1021/acs.chemrev.0c00004 Vecchio KS, Dippo OF, Kaufmann KR, Liu X. High-throughput rapid experimental alloy development (HT-READ). Acta Mater. 2021;221:117352. 10.1016/j.actamat.2021.117352 Du X, Lüer L, Heumueller T, Wagner J, Berger C, Osterrieder T, et al. Elucidating the full potential of OPV materials utilizing a high-throughput robot-based platform and machine learning. Joule. 2021;5:495-506. 10.1016/j.joule.2020.12.013 Politi M, Baum F, Vaddi K, Antonio E, Vasquez JP, Bishop B, et al. A high-throughput workflow for the synthesis of CdSe nanocrystals using a sonochemical materials acceleration platform. Digit Discov. 2023;2:1042-57. 10.1039/D3DD00033H Bayley O, Savino E, Slattery A, Noël T. Autonomous chemistry: Navigating self-driving labs in chemical and material sciences. Matter. 2024;7(7):2382-98. 10.1016/j.matt.2024.05.022 Yoshikawa N, Skreta M, Darvish K, Arellano-Rubach S, Ji Z, Bjørn Kristensen L, et al. Large language models for chemistry robotics. Auton Robots. 2023;47:1057. 10.1007/s10514-023-10136-2 Hung L, Yager JA, Potocek 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;3:1273-9. 10.1039/D4DD00059E 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;8:031406. 10.1063/5.0048164 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 Taylor CJ, Pomberger A, Felton KC, Grainger R, Barecka M, Chamberlain TW, et al. A brief introduction to chemical reaction optimization. Chem Rev. 2023;123(6):3089-126. 10.1021/acs.chemrev.2c00798 Miracle DB, Thoma DJ. Autonomous research and development of structural materials: An introduction and vision. Curr Opin Solid State Mater Sci. 2024;33:101188. 10.1016/j.cossms.2024.101188 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;8:825-36. 10.1021/acscentsci.2c00207 Volk AA, Abolhasani M. Performance metrics to unleash the power of self-driving labs in chemistry and materials science. Nat Commun. 2024;15:1378. 10.1038/s41467-024-45569-5 Tao A, Epps D, Abolhasani M, et al. Multi-objective Bayesian optimization for gold nanoparticle synthesis in microfluidics. ACS Nano. 2022;16(3):4321-30. 10.1021/acsnano.1c09876 Yoshikawa N, Darvish K, Vakili MG, Garg A, Aspuru-Guzik A. Digital pipette: Open hardware for liquid transfer in self-driving laboratories. Digit Discov. 2023;2:1745-51. 10.1039/D3DD00115F Li Y, Kirman JR, Higgins MA, et al. Robotics-accelerated crystallization of perovskites using micropipetting systems. Chem Mater. 2021;33(14):5678-86. 10.1021/acs.chemmater.1c01234 El-khawaldeh R, Guy MA, Bork F, Taherimakhsousi N, Jones KN, Hawkins J, et al. Keeping an “eye” on the experiment: Computer vision for real-time monitoring and control. Chem Sci. 2024;15:1271. 10.1039/D3SC05491H Shiri P, Lai V, Zepel T, Griffin D, Reifman J, Clark S, et al. Automated solubility screening platform using computer vision. iScience. 2021;24(3):102176. 10.1016/j.isci.2021.102176 Kearnes SM, Maser MR, Wleklinski M, Kast A, Doyle AG, Dreher SD, et al. The open reaction database. J Am Chem Soc. 2021;143:18820-6. 10.1021/jacs.1c09820 Kirman JR, Li Y, Higgins MA, et al. Closed-loop optimization of perovskite crystallization using convolutional neural networks. Nat Commun. 2020;11:4285. 10.1038/s41467-020-18045-6 Vecchio KJ, Sneddon J, Zhang Y, et al. High-throughput robotic characterization of additively manufactured alloys. Matter. 2021;4(4):1157-74. 10.1016/j.matt.2021.01.019 DeCost B, Sneddon J, Zhang Y, et al. Autonomous scanning droplet cell for accelerated discovery of corrosion-resistant alloys. ACS Cent Sci. 2022;8(5):612-23. 10.1021/acscentsci.2c00123 Higgins MA, Kirman JR, Sargent EH, et al. High-throughput synthesis and photoluminescence analysis of perovskites in a self-driving laboratory. J Am Chem Soc. 2020;142(48):20358-65. 10.1021/jacs.0c09234 Krishnadasan S, de Mello AJ, de Mello JC, et al. Microfluidics-based self-driving laboratory for nanoparticle emission optimization. Lab Chip. 2020;20(15):2789-98. 10.1039/D0LC00456A Salley DJ, Krishnadasan S, de Mello AJ, et al. Seed-mediated synthesis of gold nanoparticles using genetic algorithms in a liquid-handling platform. Anal Chem. 2021;93(10):4567-75. 10.1021/acs.analchem.0c04567 Li M, Odom SA, Pancoast AR, Robertson LA, Vaid TP, Agarwal G, et al. Experimental protocols for studying organic non-aqueous redox flow batteries. ACS Energy Lett. 2021;6:3932-43. 10.1021/acsenergylett.1c01675 Völker A, Müller T, Oberacker R, et al. Adaptive experimentation for concrete formulation optimization using modern algorithms. Cem Concr Res. 2023;167:107189. 10.1016/j.cemconres.2023.107189 Abolhasani M, Kumacheva E. The rise of self-driving labs in chemical and materials sciences. Nat Synth. 2023;2:483-92. 10.1038/s44160-023-00284-w Stokes JM, Yang K, Swanson K, Jin W, Cubillos-Ruiz A, Donghia NM, et al. A deep learning approach to antibiotic discovery. Cell. 2020;180:688-702.e13. 10.1016/j.cell.2020.01.021 Torres JAG, Lau SH, Anchuri P, Stevens JM, Tabora JE, Li J, et al. A multi-objective active learning platform and web app for reaction optimization. J Am Chem Soc. 2022;144:19999-20007. 10.1021/jacs.2c08592 Chang J, Nikolaev P, Carpena-Nunez J, Rao R, Decker K, Islam AE, et al. Efficient closed-loop maximization of carbon nanotube growth rate using Bayesian optimization. Sci Rep. 2020;10:9040. 10.1038/s41598-020-64397-3