# Machine Learning the Physical Non-Local Exchange-Correlation Functional of Density-Functional Theory

## TL;DR

This paper introduces a neural network-based exchange-correlation functional for density-functional theory that is highly non-local, computationally efficient, and consistent, aiming to improve accuracy in strongly-correlated systems.

## Contribution

It develops a fully non-local, machine-learned functional that reproduces both energy and potential, maintaining efficiency and addressing delocalization issues in DFT.

## Key findings

- Successfully models one-dimensional two-electron systems
- Maintains computational efficiency similar to local approximations
- Shows potential to improve DFT accuracy in strongly-correlated systems

## Abstract

We train a neural network as the universal exchange-correlation functional of density-functional theory that simultaneously reproduces both the exact exchange-correlation energy and potential. This functional is extremely non-local, but retains the computational scaling of traditional local or semi-local approximations. It therefore holds the promise of solving some of the delocalization problems that plague density-functional theory, while maintaining the computational efficiency that characterizes the Kohn-Sham equations. Furthermore, by using automatic differentiation, a capability present in modern machine-learning frameworks, we impose the exact mathematical relation between the exchange-correlation energy and the potential, leading to a fully consistent method. We demonstrate the feasibility of our approach by looking at one-dimensional systems with two strongly-correlated electrons, where density-functional methods are known to fail, and investigate the behavior and performance of our functional by varying the degree of non-locality.