Anatole von Lilienfeld-Toal

We want to combine modern big quantum chemistry data sets with machine learning techniques to tackle an outstanding challenge. We will develop and apply QML models capable of providing accurate predictions of chemical reactions in real-time. Accomplishing this goal would represent a major advancement for chemistry, equipping bench chemists all over the world with a tool to interactively and reliably plan their reactions prior to experimentation. The atomistic simulation community will also profit from these developments as substantially larger sets of reaction profiles will become accessible for subsequent analysis. On a conceptual level, reaching real-time speed and chemical accuracy for chemical reactions implies a profound deepening of our understanding of chemistry, and might even lead to the discovery of new chemical reactions or catalysts. It is well possible that hitherto unknown rules, akin to Hammett-relationships, will be discovered as a result of the proposed work.   Note: Grant transferred to the University of Vienna (2020), Grant transferred to other institution in 2022