In recent years, the use of machine learning (ML) in computational chemistry has enabled
numerous advances previously out of reach due to the computational complexity of …

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

In recent years, machine learning (ML) methods have become increasingly popular in
computational chemistry. After being trained on appropriate ab initio reference data, these …

Machine learning (ML) techniques applied to chemical reactions have a long history. The
present contribution discusses applications ranging from small molecule reaction dynamics …

Abstract Machine-learned force fields combine the accuracy of ab initio methods with the
efficiency of conventional force fields. However, current machine-learned force fields …

Molecular dynamics (MD) simulation techniques are widely used for various natural science
applications. Increasingly, machine learning (ML) force field (FF) models begin to replace ab …

The success of first-principles electronic-structure calculation for predictive modeling in
chemistry, solid-state physics, and materials science is constrained by the limitations on …

Rational design of compounds with specific properties requires understanding and fast
evaluation of molecular properties throughout chemical compound space—the huge set of …

Multiscale simulation and homogenization of materials have become the major
computational technology as well as engineering tools in material modeling and material …

Chemical compound space (CCS), the set of all theoretically conceivable combinations of
chemical elements and (meta-) stable geometries that make up matter, is colossal. The first …