Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

In this paper, the history, present status, and future of density-functional theory (DFT) is
informally reviewed and discussed by 70 workers in the field, including molecular scientists …

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 …

Warm dense matter (WDM)—an exotic state of highly compressed matter—has attracted
increased interest in recent years in astrophysics and for dense laboratory systems. At the …

We review state-of-the-art electronic structure methods based both on wave function theory
(WFT) and density functional theory (DFT). Strengths and limitations of both the wave …

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 …

Abstract Machine learning (ML) methods have recently been increasingly widely used in
quantum chemistry. While ML methods are now accepted as high accuracy approaches to …

The past years since the publication of our review on subsystem density‐functional theory
(sDFT)(WIREs Comput Mol Sci. 2014, 4: 325–362) have witnessed a rapid development and …

Orbital-free density functional theory (OFDFT) is a quantum chemistry formulation that has a
lower cost scaling than the prevailing Kohn–Sham DFT, which is increasingly desired for …

Materials for energy-related applications, which are crucial for a sustainable energy
economy, rely on combining materials that form complex heterogenous interfaces …