Speaker
Luca Ghiringhelli
(Fritz Haber Institute of the Max Planck Society)
Description
Using subgroup discovery (SGD), an AI approach that discovers statistically exceptional subgroups in a dataset, we develop a strategy for a rational design of catalytic materials. SGD allows for the identification of distinct, possibly competing mechanisms of a catalytic activation. Here, it is applied to the problem of converting CO$_2$ into useful chemicals. We demonstrate that the bending of CO$_2$, previously proposed as the indicator of activation, is insufficient to account for the good catalytic performance of experimentally characterized oxide surfaces. Instead, our approach identifies the asymmetric strong elongation of the molecular C-O bond as a more accurate indicator.
Primary authors
Dr
Aliaksei Mazheika
(TU Berlin)
Prof.
Sergey Levchenko
(Skolkovo Institute of Science and Technology)
Luca Ghiringhelli
(Fritz Haber Institute of the Max Planck Society)
Prof.
Matthias Scheffler
(FHI)
