The accuracy and efficiency of electronic-structure methods to understand, predict and
design the properties of materials has driven a new paradigm in research. Simulations can …

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 …

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 …

Time-dependent density functional theory continues to draw a large number of users in a
wide range of fields exploring myriad applications involving electronic spectra and …

We develop a formalism to calculate the quasiparticle energy within the GW many-body
perturbation correction to the density functional theory. The occupied and virtual orbitals of …

A comprehensive approach to modeling open quantum systems consistent with
thermodynamics is presented. The theory of open quantum systems is employed to define …

Understanding many processes, eg, fusion experiments, planetary interiors, and dwarf stars,
depends strongly on microscopic physics modeling of warm dense matter and hot dense …

Quasiparticle (QP) excitations are extremely important for understanding and predicting
charge transfer and transport in molecules, nanostructures, and extended systems. Since …

Accurate knowledge of the properties of hydrogen at high compression is crucial for
astrophysics (eg, planetary and stellar interiors, brown dwarfs, atmosphere of compact stars) …

We develop a framework for on-the-fly machine learned force field (MLFF) molecular
dynamics (MD) simulations of warm dense matter (WDM). In particular, we employ an MLFF …