Orbital-free Bond Breaking via Machine Learning

06/07/2013

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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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Orbital-free Bond Breaking via Machine Learning

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Orbital-free Bond Breaking via Machine Learning

Related Research

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DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

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