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Chemical compound space (CCS), the set of all theoretically conceivablecombinations of chemical elements and (meta-)stable geometries that make upmatter, is colossal. The first principles based virtual sampling of this space,for example in search of novel molecules or materials which exhibit desirableproperties, is therefore prohibitive for all but the smallest sub-sets andsimplest properties. We review studies aimed at tackling this challenge usingmodern machine learning techniques based on (i) synthetic data, typicallygenerated using quantum mechanics based methods, and (ii) model architecturesinspired by quantum mechanics. Such Quantum mechanics based Machine Learning(QML) approaches combine the numerical efficiency of statistical surrogatemodels with an {\em ab initio} view on matter. They rigorously reflect theunderlying physics in order to reach universality and transferability acrossCCS. While state-of-the-art approximations to quantum problems impose severecomputational bottlenecks, recent QML based developments indicate thepossibility of substantial acceleration without sacrificing the predictivepower of quantum mechanics.

Ab initio machine learning in chemical compound space