iamrp.net-N9e6-1791515265-a782294198 1791515265 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 2022 1 1 Wei Chen Li Zhang 2022 10.68159/a782294198 https://iamrp.net/pub/journal/1/article/a782294198 Zunger A. Inverse design in search of materials with target functionalities. Nat Rev Chem. 2018;2:0121. 10.1038/s41570-018-0121 Tabor DP, Roch LM, Saikin SK, Kreisbeck C, Sheberla D, Montoya JH, et al. Accelerating the discovery of materials for clean energy in the era of smart automation. Nat Rev Mater. 2018;3(5):5-20. 10.1038/s41578-018-0005-z Stach E, DeCost B, Kusne AG, Hattrick-Simpers J, Brown KA, Persson KA. Autonomous experimentation systems for materials development. Matter. 2021;4(9):2702-26. 10.1016/j.matt.2021.06.036 Häse F, Roch LM, Aspuru-Guzik A. Next-generation experimentation with self-driving laboratories. Trends Chem. 2019;1(3):282-91. 10.1016/j.trechm.2019.02.007 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. 10.1063/5.0071507 Roch LM, Häse F, Kreisbeck C, Tamayo-Mendoza T, Yunker LPE, Hein JE, et al. ChemOS: Orchestrating autonomous experimentation. Sci Robot. 2018;3(19):eaat5559. 10.1126/scirobotics.aat5559 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 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(6):1747-50. 10.1016/j.matt.2022.11.007 Soldatov MA, Butova VV, Pashkov D, Butakova MA. Self-driving laboratories for development of new functional materials and optimizing known reactions. Nanomaterials. 2021;11(3):619. 10.3390/nano11030619 Bennett JA, Abolhasani M. Autonomous chemical science and engineering enabled by self-driving laboratories. Curr Opin Chem Eng. 2022;36:100831. 10.1016/j.coche.2022.100831 Rooney MB, MacLeod BP, Oldford R, Thompson ZJ. A self-driving laboratory designed to accelerate the discovery of adhesive materials. Digit Discov. 2022;1(5):1026-37. 10.1039/D2DD00029F Tamasi MJ, Gormley AJ. Biologic formulation in a self-driving biomaterials lab. Cell Rep Phys Sci. 2022;3(7):100974. 10.1016/j.xcrp.2022.100974 Epps RW, Volk AA, Ibrahim MYS, Abolhasani M. Universal self-driving laboratory for accelerated discovery of materials and molecules. Chem. 2021;7(10):2697-719. 10.1016/j.chempr.2021.09.004 Guo Z, Yamaguchi R. Machine learning methods for protein-protein binding affinity prediction in protein design. Front Bioinform. 2022;2:1065703. 10.3389/fbinf.2022.1065703 Balachandran PV, Kowalski B, Sehirlioglu A, Lookman T. Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning. Nat Commun. 2018;9:1668. 10.1038/s41467-018-03821-9 Nicol AA, Owens SM, Le Coze SS, MacIntyre A, Eastwood C. Comparison of high-technology active learning and low-technology active learning classrooms. Act Learn High Educ. 2018;19(3):253-65. 10.1177/1469787417731176 Ling J, Hutchinson M, Antono E, Paradiso S, Meredig B. High-dimensional materials and process optimization using data-driven experimental design. Integr Mater Manuf Innov. 2017;6(3):207-17. 10.1007/s40192-017-0098-z Ament S, Yu H, Wu C, Hattrick-Simpers J, DeCost B, Sarker S, et al. Autonomous materials synthesis via hierarchical active learning of nonequilibrium phase diagrams. Sci Adv. 2021;7(51):eabg4930. 10.1126/sciadv.abg4930 Turner JM. The matter of a clean energy future. Science. 2022;376(6600):1361. Flores-Leonar MM, Mejía-Mendoza LM, Aguilar-Granda A, Sanchez-Lengeling B, Tribukait H, Amador-Bedolla C, et al. Materials acceleration platforms: On the way to autonomous experimentation. Curr Opin Green Sustain Chem. 2020;25:100370. Serrano M, Boniface M, Calisti M, Schaffers H, Domingue J, Willner A, et al. Next generation internet research and experimentation. In: Serrano M, et al. Building the future internet through FIRE. Aalborg: River Publishers; 2022. p. 43-84. Zhang R, Mozaffari A, de Pablo JJ. Autonomous materials systems from active liquid crystals. Nat Rev Mater. 2021;6(5):437-53. Gongora AE, Snapp KL, Whiting E, Riley P, Reyes KG, Morgan EF, et al. Using simulation to accelerate autonomous experimentation: A case study using mechanics. iScience. 2021;24(4). Hagnell MK, Åkermo M. The economic and mechanical potential of closed loop material usage and recycling of fibre-reinforced composite materials. J Clean Prod. 2019;223:957-68. Strieth-Kalthoff F, Sandfort F, Kühnemund M, Schäfer FR, Kuchen H, Glorius F. Machine learning for chemical reactivity: The importance of failed experiments. Angew Chem Int Ed. 2022;61(29):e202204647. Petrino R, Castrillo LG, Yilmaz B, Dodt C, Tuunainen E, Khoury A, et al. Policy statement on minimal standards for safe working conditions in Emergency Medicine. Eur J Emerg Med. 2022;29(6):389-90. National Academies of Sciences, Engineering, and Medicine. New directions for chemical engineering. Washington (DC): National Academies Press; 2022.