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.
@article{arxiv.1306.1812,
title = {Orbital-free Bond Breaking via Machine Learning},
author = {John C. Snyder and Matthias Rupp and Katja Hansen and Leo Blooston and Klaus-Robert Müller and Kieron Burke},
journal= {arXiv preprint arXiv:1306.1812},
year = {2015}
}