posted on 2022-01-11, 20:43authored bySergio Pablo-García, Albert Sabadell-Rendón, Ali J. Saadun, Santiago Morandi, Javier Pérez-Ramírez, Núria López
Activity
equations trying to mimic experimental catalytic performance
derived from reaction profiles and microkinetic models have been the
state of the art in modeling in the last decades. This approach has
been able to reproduce semiquantitatively activity volcano plots leading
to successful catalyst optimization through the use of descriptors.
As systems become more complex (both catalysts and reactants), these
methods face increasing limitations. Statistical Learning (SL) techniques
can overcome these limitations and improve the search for descriptor-based
performance equations. However, the black-box nature of SL techniques
makes physical interpretation of the so-obtained models difficult.
To advance in the integration of these methodologies to real problems,
we have merged experimental activity and selectivity presented as
a function of chemical descriptors from Density Functional Theory
for the catalyzed hydrodehalogenation of CH2X2 (for X = Br, Cl) leading to three main products. The employed Bayesian
procedure is able to identify robust equations for activity and selectivity
as a function of only two descriptors. This work provides a starting
point to solve complex reaction networks using a set of statistical
learning tools and hybrid data.