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

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 …

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

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 …

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 …

Learning from data has led to paradigm shifts in a multitude of disciplines, including web,
text and image search, speech recognition, as well as bioinformatics. Can machine learning …

Conventional machine-learning (ML) models in computational chemistry learn to directly
predict molecular properties using quantum chemistry only for reference data. While these …

Understanding interactions and structural properties at the atomic level is often a
prerequisite to the design of novel materials. Theoretical studies based on quantum …

Materials chemistry is being profoundly influenced by the uptake of machine learning
methodologies. Machine learning techniques, in combination with established techniques …