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

Abstract

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 ∼0.2 eV/Å 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.

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

“…What is the rate-limiting step in ammonia synthesis? Figure shows the generally accepted typical mechanisms of ammonia synthesis. − In the Haber–Bosch process with iron- or ruthenium-based catalysts, it is known that the reaction proceeds along the dissociative pathway, as shown in Figure a. − The rate-limiting step in this process is known as the dissociative chemisorption of N 2 . ,,, Since the computational cost of DFT calculations of transition states for estimating activation barriers is very high, it may be possible to use, for example, the reaction heat of dissociative adsorption of N 2 as an indicator of catalytic activity . In such a study, Hammond’s postulate and the Bell–Evans–Polanyi principle , are assumed.…”

“…It is an enhanced version of COMBO for materials science. Recently, many examples of BO applications in the field of materials science using PHYSBO have been reported. ,− …”

“…There are many examples of homogeneous catalysts that mimic the mechanism of catalytic conversion of nitrogen by a metalloenzyme called nitrogenase. , Heterogeneous catalytic ammonia synthesis using metal nanoparticles supported on electride surfaces − and hydrides of early transition metals such as Ti and V − has also been studied extensively. In addition, various materials such as nitrides, , alloys, , and intermetallic compounds , are being considered as potential catalysts for ammonia synthesis. Since we cannot review here all of the efforts of many researchers in creating innovative heterogeneous catalysts for ammonia synthesis, we refer the reader to some review articles − for more details.…”

Artificial Intelligence (AI) Workflow for Catalyst Design and Optimization

“…Okazawa et al demonstrate the effective combination of Bayesian optimization and density functional theory calculations to find the optimal binary alloy catalyst for nitrogen activation, a critical step in ammonia synthesis. 23 Their study showcased how Bayesian optimization surpasses random search in efficiency, highlighting the benefits of data science and computational chemistry in accelerating catalyst research, and underscores plans for future exploration of multiobjective optimization for ammonia synthesis.…”

“…They employed a computational framework combining density functional theory (DFT) calculations, machine learning-driven kinetic modeling, and Bayesian optimization, efficiently identifying optimal catalyst compositions in vast multimetallic spaces, thereby accelerating catalyst optimization processes. Okazawa et al demonstrate the effective combination of Bayesian optimization and density functional theory calculations to find the optimal binary alloy catalyst for nitrogen activation, a critical step in ammonia synthesis . Their study showcased how Bayesian optimization surpasses random search in efficiency, highlighting the benefits of data science and computational chemistry in accelerating catalyst research, and underscores plans for future exploration of multiobjective optimization for ammonia synthesis.…”