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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.
Supplementary materials
Title
Machine Learning Density Functionals SI
Description
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