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.
Show replies by date