Development of orbital-free density functional theory calculation using machine learning

Abstract

In the orbital-free density functional theory (OF-DFT), total energies in atoms and molecules are expressed as a functional of electron density. Recently, we have constructed the kinetic energy (KE) density functional (KEDF) using machine learning to reproduce the Kohn-Sham (KS) KE. The deviations in the machine-learned (ML-) KEDF from KS KE are smaller than those in any conventional KEDFs for atoms and molecules. For the practical calculations using ML-KEDF, the electron density optimization algorithm to decide the electron density in ground state from initial electron density and the kinetic potential (KP), which is the derivative of ML-KEDF in terms of electron density, are required. We developed the scheme to construct the machine learned KP (ML-KP) that corresponds to ML-KEDF, and implemented the density optimization algorithm using ML-KEDF and ML-KP. In this presentation, we will discuss the accuracies in ML-KP and optimized densities / total energies in several atoms and molecules.