기계 학습 기반 흡착에너지 예측을 통한 암모니아 분해용 합금 촉매 탐색

Abstract

Ammonia decomposition has gained significant attention as an eco-friendly method for hydrogen production because it creates no carbon dioxide emissions. While Ru catalysts are known for their high activity in ammonia decomposition, their high cost makes them uneconomical for commercial use. Therefore, it is essential to explore novel alloy catalysts composed of inexpensive elements with high catalytic performance. Nitrogen adsorption energies serve as key descriptors indicating the catalytic performance for ammonia decomposition, and first-principle calculations can compute these energies. However, the screening of numerous alloy catalyst candidates through extensive first-principle calculations and experimental validations remains time-consuming due to the vast number of potential candidates. To address this, artificial intelligence and machine learning models are being developed to quickly predict catalyst performance, efficiently searching for promising catalyst candidates. In this study, we developed a machine-learning-based method to rapidly predict nitrogen adsorption energies using a graph-based artificial neural network, thereby efficiently searching for novel catalysts for ammonia decomposition. Our training dataset included the nitrogen adsorption energies of 30 pure transition metal catalyst candidates, as well as binary alloy catalyst candidates, including core-shell and intermetallic compounds. As a result, we successfully identified 12 catalyst candidates composed of inexpensive elements that are likely to exhibit catalytic performance comparable to Ru catalysts.