摘要
The self-consistent field(SCF)generation of the three-dimensional(3D)electron density distribution(ρ)represents a fundamental aspect of density functional theory(DFT)and related first-principles calculations,and how one can shorten or bypass the SCF loop represents a critical question in electronic structure theory from both practical and fundamental standpoints.Herein,a machine learning strategy,DeepSCF,is presented in which the map between the SCFρand the initial guess density(ρ_(0))constructed by the summation of neutral atomic densities is learned using 3D convolutional neural networks(CNNs).
基金
supported by the National Research Foundation of Korea(2023R1A2C2003816 and RS-2023-00253716)
Computational resources were provided by KISTI Supercomputing Center(KSC-2023-CRE-0476).
