Convolutional network learning of self-consistent electron density via grid-projected atomic fingerprints
arxiv(2024)
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 from
both practical and fundamental standpoints. Herein, a machine learning strategy
DeepSCF is presented in which the map between the SCF ρ and the initial
guess density (ρ_0) constructed by the summation of neutral atomic
densities is learned using 3D convolutional neural networks (CNNs). High
accuracy and transferability of DeepSCF are achieved by expanding the input
features to include atomic fingerprints beyond ρ_0 and encoding them on a
3D grid. The prediction of the residual density (δρ) rather than
ρ itself is targeted, and, since δρ corresponds to chemical
bonding information, a dataset of small-sized organic molecules featuring
diverse bonding characters is adopted. After enhancing the fidelity of the
method by subjecting the atomic geometries in the dataset to random strains and
rotations, the effectiveness of DeepSCF is finally demonstrated using a complex
large carbon nanotube-based DNA sequencer model. This work evidences that the
nearsightedness in electronic structures can be optimally represented via the
local connectivity in CNNs.
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