Abstract

Many of the most relevant observables of matter depend explicitly on atomistic and electronic structure, rendering a first principles approach to chemistry and materials mandatory. Unfortunately, due to the combinatorial nature of chemical compound space, the set of all conceivably possible materials and molecules, gaining a holistic and rigorous understanding through exhaustive quantum and statistical mechanics based sampling is prohibitive—- even when using high-performance computers. Accounting for explicit and implicit dependencies and correlations, however, will not only deepen our fundamental understanding of chemical compound space but also benefit the efficiency of computational as well as experimental exploration campaigns. I will discuss recently gained insights into such relationships thanks to alchemical perturbation density functional theory and supervised machine learning. Numerical results indicate promising performance in terms of efficiency, accuracy, scalability, and transferability (EAST).