Data-driven design of new catalytic materials in methane oxidation based on a site isolation concept

TL;DR

This paper introduces a data-driven approach combining experiments, thermodynamics, and AI to discover new catalysts for methane oxidation, successfully identifying materials that outperform standard catalysts at lower temperatures.

Contribution

It presents a novel integrated method for catalyst discovery using high-throughput DFT calculations, AI screening, and experimental validation focused on the site isolation concept.

Findings

Identified new catalysts outperforming standard ones at lower temperatures.

Developed a cost-effective DFT method with 0.2 eV accuracy.

Validated the volcano-type performance dependence experimentally.

Abstract

The conversion of natural gas (methane) to ethane and ethylene (OCM: oxidative coupling of methane) facilitates its transportation and provides a way to synthesize higher value chemicals. The search for high-performance catalysts to achieve this conversion is the main scope of most corresponding studies in the field of OCM. Here, we present a general data-driven strategy for the search of novel catalytic materials, focusing particularly on materials useful for the OCM reaction. Our strategy is based on consistent experimental measurements and includes ab initio thermodynamics calculations and active screening. Based on our experiments, which showed unique 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 prediction accuracy 0.2 eV. This method was implemented into a high-throughput screening scheme, which includes both general requirements for catalyst candidates and an actively done artificial intelligence part. Experimental validation of some of the candidates obtained during the screening showed successful reproduction of the initial volcano dependence. Moreover, several new materials were found to outperform standard OCM catalysts, specifically at lower temperatures.