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Machine Learning Accurate Exchange and Correlation Functionals of the Electronic Density

revised on 12.06.2020, 17:38 and posted on 18.06.2020, 22:00 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.


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Email Address of Submitting Author


Stony Brook University



ORCID For Submitting Author


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.