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 …

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 …

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 …

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 …

We introduce a practical hybrid approach that combines orbital-free density functional theory
(DFT) with Kohn-Sham DFT for speeding up first-principles molecular dynamics simulations …

Interfaces, the boundary that separates two or more chemical compositions and/or phases of
matter, alters basic chemical and physical properties including the thermodynamics of …

This letter proposes a scheme of orbital-free density functional theory (OF-DFT) calculation
for optimizing electron density based on a semi-local machine-learned (ML) kinetic energy …

We assess several generalized gradient approximations (GGAs) and Laplacian-level meta-
GGAs (LL-MGGA) kinetic energy (KE) functionals for orbital-free density functional theory …

FE Harris has been a significant partner in our work on orbital-free density functional
approximations for use in ab initio molecular dynamics. Here we mention briefly the …