Quantum machine learning in chemical space

Please join Yale Chemistry for a Silliman Seminar in theoretical/ materials chemistry with Prof. Anatole von Lilienfeld, Professor & Clark Chair of Advanced Materials at the Vector Institute, from University of Toronto.

Summary: Many of the most relevant observables of matter depend explicitly on atomistic and electronic structure, rendering physics based approaches to chemistry and materials necessary. Unfortunately, due to the combinatorial scaling of the number of chemicals and potential reaction settings, 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 but also benefit exploration campaigns (computational and experimental). I will discuss recently gained insights from my lab elucidating such relationships thanks to alchemical perturbation density functional theory and supervised machine learning.

For more information on the von Lilienfeld Group's research: Research | chemspacelab (von Lilienfeld research)

Faculty Hosts: Professors Tianyu Zhu and Bill Jorgensen.

This seminar is generously sponsored by the Mrs. Hepsa Ely Silliman Memorial Fund.

Location: Sterling Chemistry Lab (SCL), Room 160

This seminar can be viewed online here: Panopto