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Rational design of compounds with specific properties requires conceptualunderstanding and fast evaluation of molecular properties throughout chemicalcompound space (CCS) -- the huge set of all potentially stable molecules.Recent advances in combining quantum mechanical (QM) calculations with machinelearning (ML) provide powerful tools for exploring wide swaths of CCS. Wepresent our perspective on this exciting and quickly developing field bydiscussing key advances in the development and applications of QM-based MLmethods to diverse compounds and properties and outlining the challenges ahead.We argue that significant progress in the exploration and understanding of CCScan be made through a systematic combination of rigorous physical theories,comprehensive synthetic datasets of microscopic and macroscopic properties, andmodern ML methods that account for physical and chemical knowledge.

Exploring Chemical Compound Space with Quantum-Based Machine Learning