Developing a Descriptor-Based Approach for CO and NO Adsorption Strength to Transition Metal Sites in Zeolites

The
discovery of new materials tailored for a given application
typically requires the screening of a large number of compounds, and
this process can be significantly accelerated by computational analysis.
In such an approach the performance of a compound is correlated to
a materials property, a so-called descriptor. Here we develop a descriptor-based
approach for the adsorption of CO and NO to Cu, Ni, Co, and Fe sites
in zeolites. We start out by discussing a possible design strategy
for zeolite catalysts, define the studied test set of sites in the
zeolites SSZ-13 and mordenite, and define a set of appropriate descriptors.
In a subsequent step we use these descriptors in single-parameter,
two-parameter, and multiparameter regression analysis and finally
use a machine-learning genetic algorithm to reduce the number of variables.
We find that one or two descriptors are not sufficient to accurately
capture the interactions between molecules and metal centers in zeolites,
and indeed a multiparameter approach is necessary. Even though many
of the descriptors are directly correlated, we identify the position
of the s orbital and the number of valence electrons of the active
site as well as the HOMO–LUMO gap of the adsorbate as most
important descriptors. Furthermore, the reconstruction of the active
sites upon adsorption plays a crucial role, and when it is explicitly
included in the analysis, correlations improve significantly. In the
future we expect that the fundamental methodology developed here will
be adapted and transferred to selected problems in adsorption and
catalysis and will assist the rational design of materials for the
given application.