Neural network representation of electronic structure from ab initio molecular dynamics

• 作者: Qiangqiang Gu Linfeng Zhang Ji Feng
• 来源期刊: 科学通报（英文版）
• 年，卷（期）: 2022,（1）
• 所属期刊栏目: Physical Sciences
• 页码范围: 29-37
• 页数: 9页
• 语种: 英文

摘要

Despite their rich information content,electronic structure data amassed at high volumes in ab initio molecular dynamics simulations are generally under-utilized.We introduce a transferable high-fidelity neural network representation of such data in the form of tight-binding Hamiltonians for crystalline materials.This predictive representation of ab initio electronic structure,combined with machine-learning boosted molecular dynamics,enables efficient and accurate electronic evolution and sampling.When it is applied to a one-dimension charge-density wave material,carbyne,we are able to compute the spectral function and optical conductivity in the canonical ensemble.The spectral functions evaluated during soliton-antisoliton pair annihilation process reveal significant renormalization of low-energy edge modes due to retarded electron-lattice coupling beyond the Born-Oppenheimer limit.The availability of an efficient and reusable surrogate model for the electronic structure dynamical system will enable cal-culating many interesting physical properties,paving the way to previously inaccessible or challenging avenues in materials modeling.