Bibliographic Details
| Title: |
Scalable crystal structure relaxation using an iteration-free deep generative model with uncertainty quantification |
| Authors: |
Ziduo Yang, Yi-Ming Zhao, Xian Wang, Xiaoqing Liu, Xiuying Zhang, Yifan Li, Qiujie Lv, Calvin Yu-Chian Chen, Lei Shen |
| Source: |
Nature Communications, Vol 15, Iss 1, Pp 1-15 (2024) |
| Publisher Information: |
Nature Portfolio, 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Science |
| Subject Terms: |
Science |
| More Details: |
Abstract 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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2041-1723 |
| Relation: |
https://doaj.org/toc/2041-1723 |
| DOI: |
10.1038/s41467-024-52378-3 |
| Access URL: |
https://doaj.org/article/f54538e1742442d5aca793b8115dc300 |
| Accession Number: |
edsdoj.f54538e1742442d5aca793b8115dc300 |
| Database: |
Directory of Open Access Journals |
