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

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 str

SUBMITTER: Yang Z 

PROVIDER: S-EPMC11408520 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature