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 …

Recent advances in experimental and computational methods are increasing the quantity
and complexity of generated data. This massive amount of raw data needs to be stored and …

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

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 …

Traditional force fields cannot model chemical reactivity, and suffer from low generality
without re-fitting. Neural network potentials promise to address these problems, offering …

Abstract Last year, at least 30,000 scientific papers used the Kohn–Sham scheme of density
functional theory to solve electronic structure problems in a wide variety of scientific fields …

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 …

Including prior knowledge is important for effective machine learning models in physics and
is usually achieved by explicitly adding loss terms or constraints on model architectures …

We introduce weighted atom-centered symmetry functions (wACSFs) as descriptors of a
chemical system's geometry for use in the prediction of chemical properties such as …

With the growth of computational resources, the scope of electronic structure simulations has
increased greatly. Artificial intelligence and robust data analysis hold the promise to …