典型文献
Neural network representation of electronic structure from ab initio molecular dynamics
文献摘要：
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.
文献关键词：
中图分类号：
作者姓名：
Qiangqiang Gu;Linfeng Zhang;Ji Feng
作者机构：
International Center for Quantum Materials,School of Physics,Peking University,Beijing 100871,China;Department of Mathematics and Program in Applied and Computational Mathematics,Princeton University,Princeton,NJ 08544,USA;Collaborative Innovation Center of Quantum Matter,Beijing 100871,China
文献出处：
引用格式：
[1]Qiangqiang Gu;Linfeng Zhang;Ji Feng-.Neural network representation of electronic structure from ab initio molecular dynamics)[J].科学通报（英文版）,2022(01):29-37
A类：
amassed,Hamiltonians,carbyne,antisoliton,Oppenheimer
B类：
Neural,network,representation,electronic,structure,from,initio,molecular,dynamics,Despite,their,rich,information,content,data,high,volumes,simulations,are,generally,under,utilized,We,introduce,transferable,fidelity,neural,such,tight,binding,crystalline,materials,This,predictive,combined,machine,learning,boosted,enables,efficient,accurate,evolution,sampling,When,applied,one,dimension,charge,density,wave,we,compute,spectral,optical,conductivity,canonical,ensemble,functions,evaluated,during,pair,annihilation,process,reveal,significant,renormalization,low,energy,edge,modes,due,retarded,lattice,coupling,beyond,Born,limit,availability,reusable,surrogate,dynamical,system,will,culating,many,interesting,physical,properties,paving,way,previously,inaccessible,challenging,avenues,modeling
AB值：
0.616465
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