Exploring the Optimal Alloy for Nitrogen Activation by Combining Bayesian Optimization with Density Functional Theory Calculations

Binary alloy catalysts have the potential to exhibit higher activity than monometallic catalysts in nitrogen activation reactions. However, owing to the multiple possible combinations of metal elements constituting binary alloys, an exhaustive search for the optimal combination is difficult. In this study, we searched for the optimal binary alloy catalyst for nitrogen activation reactions using a combination of Bayesian optimization and density functional theory calculations. The optimal alloy catalyst proposed by Bayesian optimization had a surface energy of similar to 0.2 eV/angstrom(2) and resulted in a low reaction heat for the dissociation of the N N bond. We demonstrated that the search for such binary alloy catalysts using Bayesian optimization is more efficient than random search.

Automated summary

In this study, a combination of Bayesian optimization and density functional theory calculations was used to search for the optimal binary alloy catalyst for nitrogen activation reactions. The results showed that using Bayesian optimization is more efficient than random search in finding the alloy catalyst.