Recent Advances in Machine Learning-Accelerated Materials Discovery — From Descriptors to Autonomous Experiments

iamrp.net-lWw3-1793296010-e225827767 1793296010 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 2024 3 1 Recent Advances in Machine Learning-Accelerated Materials Discovery — From Descriptors to Autonomous Experiments Nguyen Thanh Huy Le Thi Bich Pham Quang Minh 2024 10.68159/e225827767 https://iamrp.net/pub/journal/1/article/e225827767 Stach E, DeCost B, Kusne AG, Hattrick-Simpers J, Brown KA, Reyes KG, et al. Autonomous experimentation systems for materials development: A community perspective. Matter. 2021;4(9):2702-26. 10.1016/j.matt.2021.07.009 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 Soldatov MA, Butova VV, Pashkov D, Khaidarov RA, Kuznetsov VL, Gromov DA, et al. Self-driving laboratories for development of new functional materials and optimizing known reactions. Nanomaterials (Basel). 2021;11(3):619. 10.3390/nano11030619 Fuhr AS, Sumpter BG. Deep generative models for materials discovery and machine learning-accelerated innovation. Front Mater. 2022;9:865270. 10.3389/fmats.2022.865270 Tawfik SA, Russo SP. Naturally-meaningful and efficient descriptors: Machine learning of material properties based on robust one-shot ab initio descriptors. J Cheminform. 2022;14(1):84. 10.1186/s13321-022-00658-9 Himanen L, Jäger MOJ, Morooka EV, Foster AS, Ranawat YS, Gao J, et al. DScribe: Library of descriptors for machine learning in materials science. Comput Phys Commun. 2020;247:106949. 10.1016/j.cpc.2019.106949 Roy SK, Wind L, Johnson DD. Machine-learning-guided descriptor selection for predicting corrosion resistance in multi-principal element alloys. Npj Mater Degrad. 2022;6(1):55. 10.1038/s41529-022-00263-z Huang B, Chen H, Zou M. Machine learning identified materials descriptors for ferroelectricity. Acta Mater. 2021;209:116815. 10.1016/j.actamat.2021.116815 Oviedo F, Ferres JML, Buonassisi T. Explainable machine learning in materials science. npj Comput Mater. 2022;8(1):197. 10.1038/s41524-022-00884-7 Fung V, Hu J, Gan Q. Interpretable and Explainable Machine Learning for Materials Science and Chemistry. Acc Mater Res. 2022;3(1):1-11. 10.1021/accountsmr.1c00244 Nikolaev P, Hooper D, Webber F, Rao R, Decker K, Krein M, et al. Autonomy in materials research: a case study in carbon nanotube growth. npj Comput Mater. 2020;2:16031. 10.1038/npjcompumats.2016.31 Chen L, Peterson GB, Brand HS. Data-Driven Strategies for Accelerated Materials Design. Acc Chem Res. 2021;54(6):1355-65. 10.1021/acs.accounts.0c00785 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(1):5966. 10.1038/s41467-020-19597-w Noack MM, Doerk GS, Li R. Autonomous materials synthesis via hierarchical active learning of nonequilibrium phase diagrams. Sci Adv. 2021;7(51):eabg4930. 10.1126/sciadv.abg4930 New A, Le NQ, Pekala MJ, Stiles CD, Self-supervised learning for fast and scalable property predictions in crystals. arXiv preprint arXiv:2202.11237. 2022. Dan Y, Zhao Y, Li X. Generative inverse design of crystal structures via diffusion models with transformers. arXiv preprint arXiv:2209.08677. 2022. 10.48550/arXiv.2209.08677 Frey NC, Soklaski R, Axelrod S, Samsi S, Gómez-Bombarelli R, Coley CW, et al. Neural scaling of deep chemical models. Nat Mach Intell. 2023;5:1297–305. 10.1038/s42256-023-00740-3 Merchant A, Batzner S, Schoenholz SS, Aykol M, Cheon G, Cubuk ED. Scaling deep learning for materials discovery. Nature. 2021;624:80-5. 10.1038/s41586-023-06735-9 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 Court CJ, Yildirim B, Jain A. Stability and defect chemistry of lead halide perovskites from machine learning. Chem Mater. 2020;32(9):3752-62. 10.1021/acs.chemmater.9b05367 Ludwig J, Zschech E. Federated learning approaches for distributed materials data and learning: A review. Comput Mater Sci. 2022;204:111161. 10.1016/j.commatsci.2021.111161 Ludwig J, Boley M, Müller KR. Autonomous experiments in scanning probe microscopy and spectroscopy: Choosing where to explore polarization phenomena in ferroelectrics. ACS Nano. 2021;15(8):12763-73. 10.1021/acsnano.1c02177