Machine Learning approaches to classical density functional theory

TL;DR

This paper reviews recent machine learning methods applied to classical density functional theory, highlighting their potential to improve free energy functionals and exploring future opportunities across related fields.

Contribution

It introduces how machine learning can enhance the construction of free energy functionals in classical density functional theory and discusses future research directions.

Findings

ML methods can improve free energy functional construction

Potential for ML to complement traditional approaches

Outlook on ML applications in related theories

Abstract

In this chapter, we discuss recent advances and new opportunities through methods of machine learning for the field of classical density functional theory, dealing with the equilibrium properties of thermal nano- and micro-particle systems having classical interactions. Machine learning methods offer the great potential to construct and/or improve the free energy functional (the central object of density functional theory) from simulation data and thus they complement traditional physics- or intuition-based approaches to the free energy construction. We also give an outlook to machine learning efforts in related fields, such as liquid state theory, electron density functional theory and power functional theory as a functionally formulated approach to classical nonequilibrium systems.