Dear colleagues,
Just a quick reminder about tomorrow's PChem seminar with Anatole von Lilienfeld at 4:15pm in Pfizer Auditorium.
Best wishes,
-Martin
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 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.