Data-driven Design of Catalytic Materials in Methane Oxidation Based on a Site Isolation Concept

We present a general data-driven strategy for the search
for catalytic
materials, focusing particularly on materials useful for the conversion
of natural gas (methane) to ethane and ethylene (OCM: oxidative coupling
of methane reaction). OCM facilitates the transportation of natural
gas and provides a way to synthesize higher-value chemicals. Our strategy
is based on consistent experimental measurements and includes ab initio
thermodynamics calculations and active screening.
Based on our experiments, which showed a volcano-type dependence of
the performance on the stability of formed carbonates attributed to
the site isolation concept, we developed a method for efficient and
inexpensive DFT calculations of the formation energies of carbonates
with a prediction accuracy of 0.2 eV based on the Boltzmann distribution
of surface terminations. This method was implemented into a high-throughput
screening scheme, which includes both general requirements for catalyst
candidates and an actively performed artificial intelligence part.
Guided by theoretical predictions, we have performed experimental
validation of some of the candidates obtained during the screening
which showed successful reproduction of the initial volcano dependence.
Predicted in this way, materials were found to show in general comparable
performance to well-known standard OCM catalysts, or even higher yields
specifically at temperatures between 700 and 800 °C.