iamrp.net-LVfc-1791515266-z204044064 1791515266 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 Bruno Martins Lucas Pereira 2025 10.68159/z204044064 https://iamrp.net/pub/journal/2/article/z204044064 Cheetham AK, Seshadri R. Artificial intelligence driving materials discovery? Perspective on the article: Scaling deep learning for materials discovery. Chem Mater. 2024;36(8):3490-5. Hatakeyama-Sato K, Yamane N, Igarashi Y, Nabae Y, Hayakawa T. Data-driven design of polymer dielectrics using machine learning. Sci Technol Adv Mater Methods. 2023;3(1):2260300. Pilania G. Machine learning in materials science: From explainable predictions to autonomous design. Comput Mater Sci. 2021;193:110360. Flores-Léonar MM, Mejía-Mendoza LM, Domínguez-Hüttinger E, Vivas-Valencia C, Rodríguez-González A, Zepeda-Pedreguera A, et al. Materials Acceleration Platforms: On the way to autonomous experimentation. Curr Opin Green Sustain Chem. 2020;25:100370. Wagner J, Berger F, Lanzerath F, Becker B, Fink RH, Sciana M, et al. The evolution of materials acceleration platforms: toward the laboratory of the future with AMANDA. J Mater Scie. 2021;56(24):16422-46. Koscher BA, Gomes GDP, Yoshikawa N, Martinez R, Au J, Seifrid M, et al. Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back. Science. 2023;382(6667):eadi1407. MacLeod BP, Parlane FGL, Rupnow CC, Dettlebach KE, Elliott MS, Morrissey TD, et al. A self-driving laboratory advances the Pareto front for material properties. Nat Commun. 2022;13(1):995. Hanaoka K. Bayesian optimization for goal-oriented multi-objective inverse material design. iScience. 2021;24(10):102781. Bran AM, Cox SC, Schaffner M, Kalpathy SV, Gosau T, Reischl U, et al. Augmenting large language models with chemistry tools. Nat Mach Intell. 2024;6(6):525-35. Yik JT, Tong A, Parrott A, Moreira R, Sarrat E, Breen C, et al. Accelerating aqueous electrolyte discovery with automated full-cell battery experimentation and Bayesian optimization. Cell Rep Phys Sci. 2025;6(2):102548. Chen Q, Wu H, Li S, Xie C, Wu Y, Wang J, et al. Rapid synthesis of metastable materials for electrocatalysis. Chem Soc Rev. 2025;54(10):4567-616. Seifrid M, Strieth-Kalthoff F, Haddadnia M, Wu TC, Alca E, Bodo L, et al. Autonomous chemical experiments: Challenges and perspectives on establishing a self-driving lab. Acc Chem Res. 2022;55(17):2454-66. Pyzer-Knapp EO, Pitera JW, Staar PWJ, Takeda S, Laino T, Sanders DP, et al. Accelerating materials discovery using artificial intelligence, high performance computing and robotics. npj Comput Mater. 2022;8(1):1-9. Tom G, Schmid SP, Baird SG, Cao Y, Darvish K, Hao H, et al. Chem Rev. 2024;124(15):9633-732. Lunt AM, Mehr SHM, Badman B, Beaver E, Cronin L. Modular, multi-robot integration of laboratories: An autonomous workflow for solid-state chemistry. Chem Sci. 2024;15(8):2456-63. Szymanski NJ, Bartolomei B, Briling M, Huang YI, Loeffler S, Mosquera-Romero C, et al. An autonomous laboratory for the accelerated synthesis of novel materials. Nature. 2023;624(7992):86-91. Angello NH, Rathore V, Beker W, Wołos A, Moosavi SM, Roszak R, et al. Closed-loop optimization of general reaction conditions for heteroaryl Suzuki-Miyaura coupling. Science. 2022;378(6617):399-405. Granda JM, Donina L, Dragone V, Long DL, Cronin L. Controlling an organic synthesis robot with machine learning to search for new reactivity. Nature. 2018;559(7714):377-81. Ha T, Lee IB, Jeong H, McGinty N, Liu J, Stein HS, et al. AI-driven robotic chemist for autonomous synthesis of organic molecules. Sci Adv. 2023;9(10):eadj0461. Deneault JR, Kanov B, Motezak M, Cintins A, Telama J, Kotamäki N, et al. Toward autonomous additive manufacturing: Bayesian optimization on a 3D printer. MRS Bull. 2021;46(7):566-75. Rooney MB, Yunker LPE, Christensen M, Coley CW, Hein JE. A self-driving laboratory designed to accelerate the discovery of adhesive materials. Digit Discov. 2022;1(6):382-9. Elder S, Grantham K, Rudman L, Gunawan G, Gormley AJ. Cross-platform Bayesian optimization system for autonomous biological assay development. SLAS Technol. 2021;26(5):579-90. Di Fiore F, Tomforde S. Active learning and Bayesian optimization: a unified perspective to learn with a goal. Arch Comput Methods Eng. 2024;31(7):2985-3013. Li J, Lu Y, Xu Y, Liu C, Tu Y, Ye S, et al. AIR-Chem: Authentic intelligent robotics for chemistry. J Phys ChemA. 2018;122(46):9142-8. 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(6694):eadk9227. Rapp JT, Breiner B, Gormley AJ, Thompson DR, Long A, Seddon D, et al. Self-driving laboratories to autonomously navigate the protein fitness landscape. Nat Chem Eng. 2024;1(2):97-107. Ishizuki N, Shimizu R, Hitosugi T. Autonomous experimental systems in materials science. Sci Technol Adv Mater Methods. 2023;3(1):2197519. Vogler M, Berkemeier F, Hielscher MM, Schlaad H, Schacher FH, Bäumler H, et al. Brokering between tenants for an international materials acceleration platform. Matter. 2023;6(8):2647-65. Greenaway RL, Mehr SHM, Cronin L. From alchemist to AI chemist. Nat Rev Chem. 2023;7(9):527-8. Doloi S, Shimono H, Mitsuyama T, Horinouchi T. Democratizing self-driving labs: advances in low-cost 3D printing for laboratory automation. Digit Discov.2025;4(7):1685-721. 10.1039/D4DD00411F Nolte L, Tomforde S. A helping hand: a survey about AI-driven experimental design for accelerating scientific research. Appl Sci. 2025;15(9):5208. Machi K, Akiyama S, Nagata Y, Yoshioka M. A framework for reviewing the results of automated conversion of structured organic synthesis procedures from the literature. Digit Discov. 2025;4(1):172-80. 10.1039/d4dd00335g