Dear colleagues, Below are the details for the pchem seminar with Anatole von Lilienfeld next Thursday, Jan 30th. Best wishes, -Martin *Date:* Thursday, Jan 30, 4:15pm, Pfizer (coffee & cookies at 4pm) *Title:*" 'Quantum Machine': Supervised learning of Schrödinger's equation in chemical compound space" *Abstract:* 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 of all the possible compounds 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. I will discuss recent contributions related to the former aspect. More specifically, we developed statistical models of quantum mechanical observables based on supervised learning in chemical space. Our results suggest that for those out-of-sample molecules that lie in interpolating regimes of chemical space, a predictive accuracy can be achieved that comes close to ``chemical accuracy'' (~1 kcal/mol), highly sought-after in thermochemistry, at a fraction of the computational cost.