iamrp.net-zoPX-1791515264-f785636664 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 2 Nguyen Thanh Huy Pham Quang Minh Le Thi Bich 2025 10.68159/f785636664 https://iamrp.net/pub/journal/2/article/f785636664 Merchant A, Batzner S, Schoenholz SS, Aykol M, Cheon G, Cubuk ED, et al. Scaling deep learning for materials discovery. Nature. 2023;624(7990):80-5. 10.1038/s41586-023-06735-9 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 Saal JE, Kirklin S, Aykol M, Meredig B, Wolverton C. Materials design and discovery with high-throughput density functional theory: The open quantum materials database (OQMD). Jom. 2013;65(11):1501-9. Schmidt J, Marques MRG, Botti S, Marques MAL. Recent advances and applications of machine learning in solid-state materials science. npj Comput Mater. 2019;5:83. 10.1038/s41524-019-0221-0 Tshitoyan V, Dagdelen J, Weston L, Dunn A, Rong Z, Kononova O, et al. Unsupervised word embeddings capture latent knowledge from materials science literature. Nature. 2019;571(7763):95-8. 10.1038/s41586-019-1335-8 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 Szymanski NJ, Rendy B, Fei Y, Kumari R, McDermott MJ, Gallant M, et al. An autonomous laboratory for the accelerated synthesis of inorganic materials. Nature. 2023;624(7990):86-91. 10.1038/s41586-023-06734-w 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):84. 10.1038/s41524-022-00765-z 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 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. 10.1371/journal.pone.0229862 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;13:5454. 10.1038/s41467-022-32938-1 Abolhasani M, Kumacheva E. The rise of self-driving labs in chemical and materials sciences. Nat Synth. 2023;2(5):483-92. 10.1038/s44160-022-00231-0 Lin MY, Severson K, Grandgeorge P, Roumeli E. Closed-loop Bayesian optimization for materials acceleration platforms. Adv Intell Syst. 2022;4(5):2100230. Meredig B. Validation latency in high-throughput DFT/ML pipelines for accelerated materials discovery. Comput Mater Sci. 2024;245:113245. Lookman T, Balachandran PV, Xue D, Yuan R. Active learning in materials science with machine learning and sequential experimental design. npj Comput Mater. 2019;5:112. 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;10:17663. 10.1038/s41598-020-74394-1 Häse F, Roch LM, Aspuru-Guzik A. Chimera: Enabling hierarchy based multi-objective optimization for self-driving laboratories. Chem Sci. 2018;9(39):7642-55. 10.1039/C8SC02239A Zunger A. Inverse design in materials science: the new frontier of accelerated materials discovery. npj Comput Mater. 2021;7:1-10. Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A. Machine learning for molecular and materials science. Nature. 2018;559(7715):547-55. 10.1038/s41586-018-0337-2 Jablonka KM, Jothilingam R, et al. Machine learning for materials discovery in the era of foundation models. Digit Discov. 2024;3:1234-50. 10.1039/D4DD00012A Pyzer-Knapp EO, Manica M, Staar P, Morin L, Ruch P, Laino T, et al. Foundation models for materials discovery. npj Comput Mater. 2025;11:45. 10.1038/s41524-025-01538-0 Delgado-Licona F, Alsaiari A, Dickerson H, Klem P, Ghorai A, Canty RB, et al. Flow-driven data intensification to accelerate autonomous materials discovery. Nat Chem Eng. 2025;2:123-35. Sparks TD, Kauwe SK, Parry ME, Tehrani AM, Brgoch J. Data-driven discovery of high-performance layered van der Waals piezoelectric materials. Chem Mater. 2018;30(14):4668-77. Chen C, Zuo Y, Ye W, Li X, Ong SP. A critical review of machine learning of energy materials. Adv Energy Mater. 2020;10(8):1903245. Lu S, Zhou Q, Ma L, Guo Y, Wang J. Accelerated discovery of stable and efficient single-atom catalysts for N2 fixation by machine learning. Adv Funct Mater. 2021;31(31):2101856. Schmidt J. Machine learning in materials science: From discovery to design. Matter. 2023;6(10):3125-50.