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Orbital-free Bond Breaking via Machine Learning (1306.1812v1)
Published 7 Jun 2013 in physics.chem-ph, cond-mat.mtrl-sci, and stat.ML
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.