Efficient Learning of a One-dimensional Density Functional Theory

Understand

Density functional theory underlies the most successful and widely used numerical methods for electronic structure prediction of solids.

• However, it has the fundamental shortcoming that the universal density functional is unknown.
• In addition, the computational result---energy and charge density distribution of the ground state---is useful for electronic properties of solids mostly when reduced to a band structure interpretation based on the Kohn-Sham approach.
• Here, we demonstrate how machine learning algorithms can help to free density functional theory from these limitations.