Advances in machine learning methods provide tools that have broad applicability in
scientific research. These techniques are being applied across the diversity of nuclear …

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 …

Density functional theory describes matter at the quantum level, but all popular
approximations suffer from systematic errors that arise from the violation of mathematical …

Electronically excited states of molecules are at the heart of photochemistry, photophysics,
as well as photobiology and also play a role in material science. Their theoretical description …

Kohn-Sham density functional theory (DFT) is a standard tool in most branches of chemistry,
but accuracies for many molecules are limited to 2-3 kcal⋅ mol− 1 with presently-available …

In recent years, we have been witnessing a paradigm shift in computational materials
science. In fact, traditional methods, mostly developed in the second half of the XXth century …

Abstract Machine learning advances chemistry and materials science by enabling large-
scale exploration of chemical space based on quantum chemical calculations. While these …

Advances in machine learning have impacted myriad areas of materials science, such as
the discovery of novel materials and the improvement of molecular simulations, with likely …

Quantum matter, the research field studying phases of matter whose properties are
intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter …

Machine learning (ML) has emerged as a formidable force for identifying hidden but
pertinent patterns within a given data set with the objective of subsequent generation of …