Simulations based on solving the Kohn-Sham(KS)equation of density functional theory(DFT)have become a vital component of modern materials and chemical sciences research and development portfolios.Despite its versatili...Simulations based on solving the Kohn-Sham(KS)equation of density functional theory(DFT)have become a vital component of modern materials and chemical sciences research and development portfolios.Despite its versatility,routine DFT calculations are usually limited to a few hundred atoms due to the computational bottleneck posed by the KS equation.Here we introduce a machine-learning-based scheme to efficiently assimilate the function of the KS equation,and by-pass it to directly,rapidly,and accurately predict the electronic structure of a material or a molecule,given just its atomic configuration.A new rotationally invariant representation is utilized to map the atomic environment around a grid-point to the electron density and local density of states at that grid-point.This mapping is learned using a neural network trained on previously generated reference DFT results at millions of grid-points.The proposed paradigm allows for the high-fidelity emulation of KS DFT,but orders of magnitude faster than the direct solution.Moreover,the machine learning prediction scheme is strictly linear-scaling with system size.展开更多
基金The authors would like to thank XSEDE for the utilization of Stampede2 cluster via project ID“DMR080058N”This work is supported by the Office of Naval Research through N0014-17-1-2656,a Multi-University Research Initiative(MURI)grant.
文摘Simulations based on solving the Kohn-Sham(KS)equation of density functional theory(DFT)have become a vital component of modern materials and chemical sciences research and development portfolios.Despite its versatility,routine DFT calculations are usually limited to a few hundred atoms due to the computational bottleneck posed by the KS equation.Here we introduce a machine-learning-based scheme to efficiently assimilate the function of the KS equation,and by-pass it to directly,rapidly,and accurately predict the electronic structure of a material or a molecule,given just its atomic configuration.A new rotationally invariant representation is utilized to map the atomic environment around a grid-point to the electron density and local density of states at that grid-point.This mapping is learned using a neural network trained on previously generated reference DFT results at millions of grid-points.The proposed paradigm allows for the high-fidelity emulation of KS DFT,but orders of magnitude faster than the direct solution.Moreover,the machine learning prediction scheme is strictly linear-scaling with system size.
