Pushing the frontiers of density functionals by solving the fractional electron problem

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Pushing the frontiers of density functionals by solving the fractional electron problem
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Neural networks can be used to build a more accurate map of the density and interaction between electrons than was previously attainable, new research from DeepMind shows. Learn more ⬇ ScienceResearch: SciencePerspective:

In the past 30 years, density functional theory has emerged as the most widely used electronic structure method to predict the properties of various systems in chemistry, biology, and materials science. Despite a long history of successes, state-of-the-art DFT functionals have crucial limitations. In particular, significant systematic errors are observed for charge densities involving mobile charges and spins. Kirkpatrick.

—YSDensity functional theory describes matter at the quantum level, but all popular approximations suffer from systematic errors that arise from the violation of mathematical properties of the exact functional. We overcame this fundamental limitation by training a neural network on molecular data and on fictitious systems with fractional charge and spin.

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