Orbital-free Bond Breaking via Machine Learning

Abstract

Machine learning is used to approximate the kinetic energy of one dimensional diatomics as a functional of the electron density. The functional can accurately dissociate a diatomic, and can be systematically improved with training. Highly accurate self-consistent densities and molecular forces are found, indicating the possibility for ab-initio molecular dynamics simulations.

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• Understanding and reducing errors in density functional calculations
• The discontinuous nature of the exchange-correlation functional -- critical for strongly correlated systems
• Exact condition on the Kohn-Sham kinetic energy, and modern parametrization of the Thomas-Fermi density

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