  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.
