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Predicting the One-Particle Density Matrix with Machine Learning
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DOI：10.1021/acs.jctc.4c00042.png)
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摘要
En 中文
Two of the most widely used electronic-structure theory methods, namely, Hartree-Fock and Kohn-Sham density functional theory, require the iterative solution of a set of Schrodinger-like equations. The speed of convergence of such a process depends on the complexity of the system under investigation, the self-consistent-field algorithm employed, and the initial guess for the density matrix. An initial density matrix close to the ground-state matrix will effectively allow one to cut out many of the self-consistent steps necessary to achieve convergence. Here, we predict the density matrix of Kohn-Sham density functional theory by constructing a neural network that uses only the atomic positions as information. Such a neural network provides an initial guess for the density matrix far superior to that of any other recipes available. Furthermore, the quality of such a neural-network density matrix is good enough for the evaluation of interatomic forces. This allows us to run accelerated ab initio molecular dynamics with little to no self-consistent steps.
Keyword:
AB-INITIO
SCF CALCULATIONS
CONVERGENCE
EXCHANGE
EQUATIONS
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期刊
IF:
5.5
论文数:
1.1W
被引数:
5.4W
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2016 International Conference on Advanced Communication Control and Computing Technologies (ICACCCT)
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