Machine Learning Accurate Exchange and Correlation Functionals of the Electronic Density

18 June 2020, Version 3
This content is a preprint and has not undergone peer review at the time of posting.


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.


Neural Networks
Molecular Dynamics
Density Functional Theory

Supplementary materials

Machine Learning Density Functionals SI


Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.