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Machine learning accurate exchange and correlation functionals of the electronic density.

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 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.

journal_name

Nat Commun

journal_title

Nature communications

authors

Dick S,Fernandez-Serra M

doi

10.1038/s41467-020-17265-7

subject

Has Abstract

pub_date

2020-07-14 00:00:00

pages

3509

issue

1

issn

2041-1723

pii

10.1038/s41467-020-17265-7

journal_volume

11

pub_type

杂志文章

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