Professor O. Anatole von Lilienfeld, University of Basel, Argonne Leadership Computing Facility, Argonne National Laboratory.  "Quantum Machine": Supervised learning of Schrödinger's equation in chemical compound space.  R.B. Woodward Lectures in the Chemical Sciences, Physical Chemistry Seminar

Many of the most relevant chemical properties of matter depend explicitly on atomistic details, rendering an atomistic resolution of any employed simulation model mandatory. Alas, even when using high-performance computing, brute force high-throughput screening is beyond any capacity for all but the simplest systems and properties due to the combinatorial nature of chemical compound space (compositional, constitutional, and conformational isomers). Consequently, when it comes to the computational design of properties or systems that require first principles calculations, a successful optimization algorithm must not only make a trade-off between sufficient accuracy of applied models and computational speed, but must also aim for rapid convergence in terms of number of compounds visited. In this talk I will discuss recent contributions related to the former aspect. More specifically, we developed statistical models to predict quantum mechanical observables based on supervised learning of the electronic Schrodinger equation in chemical space. Our results suggest that out-of-sample molecules in interpolating regimes of chemical space can be predicted with an accuracy that comes close to ``chemical accuracy'' (~1 kcal/mol), highly sought-after in thermo-chemistry and other branches of chemistry, at a fraction of the computational cost.