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Machine Learning Accurate Exchange and Correlation Functionals of the Electronic Density

preprint
revised on 12.06.2020 and posted on 18.06.2020 by Sebastian Dick, Marivi Fernandez-Serra
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.

Funding

Development and Application of Methods for understanding Interfacial Charge Transfer in Photocatalytic Water Splitting Materials

Basic Energy Sciences

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Computational Chemical Science Center: Chemistry in Solution and at Interfaces

Basic Energy Sciences

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ACI-1547580

History

Email Address of Submitting Author

sebastian.dick@stonybrook.edu

Institution

Stony Brook University

Country

USA

ORCID For Submitting Author

0000-0002-9316-4586

Declaration of Conflict of Interest

The authors declare that there is not conflict of interest

Version Notes

Added/modified some figures and fixed notation in equations.

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