Kohn–Sham Density Functional Theory (KSDFT) is the most widely used electronic structure
method in chemistry, physics, and materials science, with thousands of calculations cited …

This article is a rough, quirky overview of both the history and present state of the art of
density functional theory. The field is so huge that no attempt to be comprehensive is made …

Orbital-free density functional theory (OFDFT) is both grounded in quantum physics and
suitable for direct simulation of thousands of atoms. This article describes the application of …

Machine learning (ML) is an increasingly popular statistical tool for analyzing either
measured or calculated data sets. Here, we explore its application to a well‐defined physics …

The electron density determines all properties of a system of nuclei and electrons. It is both
computable and observable. Its topology allows gaining insight into the mechanisms of …

Most computational studies in chemistry and materials science are based on the use of
density functional theory. Although the exact density functional is unknown, several density …

We demonstrate a convolutional neural network trained to reproduce the Kohn–Sham
kinetic energy of hydrocarbons from an input electron density. The output of the network is …

Using a one-dimensional model, we explore the ability of machine learning to approximate
the non-interacting kinetic energy density functional of diatomics. This nonlinear …

A simple, unconventional, nonempirical, constraint-based orbital-free generalized gradient
approximation (GGA) noninteracting kinetic energy density functional is presented along …

Solving the Euler equation which corresponds to the energy minimum of a density functional
expressed in orbital-free form involves related but distinct computational challenges. One is …