Combinatorial Computational Chemistry Approach to the High-Throughput Screening of Metal Sulfide Catalysts for CO Hydrogenation Process

Abstract

We have already proposed that a “Combinatorial Computational Chemistry” approach is very effective for performing the theoretical high-throughput screening of new catalysts, and its validity was strongly confirmed in various catalyst systems. In the present study, we applied our combinatorial computational chemistry approach to the design of new metal sulfide catalysts for the CO hydrogenation process and proposed new guidance for designing the highly selective catalysts for methanol synthesis. We investigated H2 and CO adsorption on a large number of metal and metal sulfide catalysts by first-principles calculations, and succeeded in clarifying the relationship between the metal species in the metal and metal sulfide catalysts and the products of the CO hydrogenation processes. Our results indicated that Co, Mo, Ru, Rh, Ir, and Pd sulfide catalysts selectively produce methanol, while Re and Os sulfide catalysts selectively produce hydrocarbons. The above results are in good agreement with the experimental results of Koizumi and co-workers. Moreover, we proposed that the Pd sulfide catalyst has the highest selectivity for methanol from the CO hydrogenation process. This result strongly supports the experimental results by Koizumi and co-workers. Moreover, we propose that the metal sulfide catalysts, which realize the bridge-site adsorption of the CO molecule on both the metal and sulfur atoms, have high selectivity for methanol. This proposed guidance for designing the highly selective metal sulfide catalysts for methanol may be useful for the experiments.