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 …

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

First-principles-based exploration of chemical space deepens our understanding of
chemistry and might help with the design of new molecules, materials or experiments. Due …

In this perspective, we discuss computational advances in the last decades, both in
algorithms as well as in technologies, that enabled the development, widespread use, and …

Standfirst Over the last decade, we have witnessed the emergence of ever more machine
learning applications in all aspects of the chemical sciences. Here, we highlight specific …

We introduce QM7-X, a comprehensive dataset of 42 physicochemical properties for≈ 4.2
million equilibrium and non-equilibrium structures of small organic molecules with up to …

Abstract Machine learning (ML) methods enable computers to address problems by learning
from existing data. Such applications are becoming commonplace in molecular sciences …

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

Abstract Machine learning (ML) algorithms are currently emerging as powerful tools in all
areas of science. Conventionally, ML is understood as a fundamentally data‐driven …

High-throughput computational screening for chemical discovery mandates the automated
and unsupervised simulation of thousands of new molecules and materials. In challenging …