Machine Learning Accurate Exchange and Correlation Functionals of the Electronic Density

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18 June 2020, Version 3

Working Paper

This content is an early or alternative research output and has not been peer-reviewed by Cambridge University Press at the time of posting.

Abstract

Density Functional Theory (DFT) is the standard formalism to study the electronic structure

of matter at the atomic scale. In Kohn-Sham DFT simulations, the balance between accuracy

and computational cost depends on the choice of exchange and correlation functional, which only

exists in approximate form. Here we propose a framework to create density functionals using

supervised machine learning, termed NeuralXC. These machine-learned functionals are designed to

lift the accuracy of baseline functionals towards that are provided by more accurate methods while

maintaining their efficiency. We show that the functionals learn a meaningful representation of the

physical information contained in the training data, making them transferable across systems. A

NeuralXC functional optimized for water outperforms other methods characterizing bond breaking

and excels when comparing against experimental results. This work demonstrates that NeuralXC

is a first step towards the design of a universal, highly accurate functional valid for both molecules

and solids.

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Machine Learning Density Functionals SI

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