Orbital-free Bond Breaking via Machine Learning
This work addresses the challenge of simulating molecular dynamics with high accuracy in computational chemistry, representing an incremental improvement in orbital-free density functional theory.
The authors tackled the problem of accurately modeling bond dissociation in diatomic molecules by using machine learning to approximate kinetic energy as a functional of electron density, achieving highly accurate self-consistent densities and molecular forces that enable ab-initio molecular dynamics simulations.
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.