Exploring Metal Nanocluster Catalysts for Ammonia Synthesis Using Informatics Methods: A Concerted Effort of Bayesian Optimization, Swarm Intelligence, and First-Principles Computation

This paper details the use of computational and informaticsmethodsto design metal nanocluster catalysts for efficient ammonia synthesis.Three main problems are tackled: defining a measure of catalytic activity,choosing the best candidate from a large number of possibilities,and identifying the thermodynamically stable cluster catalyst structure.First-principles calculations, Bayesian optimization, and particleswarm optimization are used to obtain a Ti-8 nanoclusteras a catalyst candidate. The N-2 adsorption structure onTi(8) indicates substantial activation of the N-2 molecule, while the NH3 adsorption structure suggeststhat NH3 is likely to undergo easy desorption. The studyalso reveals several cluster catalyst candidates that break the generaltrade-off that surfaces that strongly adsorb reactants also stronglyadsorb products.

Automated summary

This paper discusses the use of computational and informatics approaches to design efficient metal nanocluster catalysts for ammonia synthesis. It addresses three main challenges: measuring catalytic activity, selecting the best catalyst from numerous candidates, and identifying the thermodynamically stable cluster catalyst structure. The study employs first-principles calculations, Bayesian optimization, and particle swarm optimization to identify a Ti-8 nanocluster as a potential catalyst candidate. The adsorption structures of N-2 and NH3 on Ti(8) indicate significant activation of N-2 and easy desorption of NH3. The research also discovers cluster catalyst candidates that defy the conventional trade-off between strong adsorption of reactants and products.