Scalable crystal structure relaxation using an iteration-free deep generative model with uncertainty quantification.

Autor: Yang Z; Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore.; Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China.; AI for Science (AI4S)-Preferred Program, School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School, Shenzhen, China., Zhao YM; Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore., Wang X; Department of Physics, National University of Singapore, Singapore, Singapore., Liu X; Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore., Zhang X; Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore., Li Y; Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore., Lv Q; Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore.; Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China., Chen CY; AI for Science (AI4S)-Preferred Program, School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School, Shenzhen, China. cy@pku.edu.cn.; State Key Laboratory of Chemical Oncogenomics, School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School, Shenzhen, China. cy@pku.edu.cn.; Department of Medical Research, China Medical University Hospital, Taichung, Taiwan. cy@pku.edu.cn.; Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan. cy@pku.edu.cn.; Guangdong L-Med Biotechnology Co., Ltd., Meizhou, Guangdong, China. cy@pku.edu.cn., Shen L; Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore. shenlei@nus.edu.sg.; National University of Singapore (Chongqing) Research Institute, Chongqing, China. shenlei@nus.edu.sg.
Jazyk: angličtina
Zdroj: Nature communications [Nat Commun] 2024 Sep 17; Vol. 15 (1), pp. 8148. Date of Electronic Publication: 2024 Sep 17.
DOI: 10.1038/s41467-024-52378-3
Abstrakt: In computational molecular and materials science, determining equilibrium structures is the crucial first step for accurate subsequent property calculations. However, the recent discovery of millions of new crystals and super large twisted structures has challenged traditional computational methods, both ab initio and machine-learning-based, due to their computationally intensive iterative processes. To address these scalability issues, here we introduce DeepRelax, a deep generative model capable of performing geometric crystal structure relaxation rapidly and without iterations. DeepRelax learns the equilibrium structural distribution, enabling it to predict relaxed structures directly from their unrelaxed ones. The ability to perform structural relaxation at the millisecond level per structure, combined with the scalability of parallel processing, makes DeepRelax particularly useful for large-scale virtual screening. We demonstrate DeepRelax's reliability and robustness by applying it to five diverse databases, including oxides, Materials Project, two-dimensional materials, van der Waals crystals, and crystals with point defects. DeepRelax consistently shows high accuracy and efficiency, validated by density functional theory calculations. Finally, we enhance its trustworthiness by integrating uncertainty quantification. This work significantly accelerates computational workflows, offering a robust and trustworthy machine-learning method for material discovery and advancing the application of AI for science.
(© 2024. The Author(s).)
Databáze: MEDLINE
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