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 networ...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 machinelearning 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 calculating many interesting physical properties,paving the way to previously inaccessible or challenging avenues in materials modeling.展开更多
基金supported by the National Natural Science Foundation of China(11725415 and 11934001)the Ministry of Science and Technology of China(2018YFA0305601 and2016YFA0301004)+1 种基金by the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB28000000)supported in part by the Center for Chemistry in Solution and at Interfaces(CSI)at Princeton University,funded by the DOE Award DE-SC0019394。
文摘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 machinelearning 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 calculating many interesting physical properties,paving the way to previously inaccessible or challenging avenues in materials modeling.
