Catalysis informatics is constantly developing, and significant advances in data mining, molecular simulation, and automation for computational design and high-throughput experimentation have been achieved. However, efforts to reveal the mechanisms of complex supported nanoparticle catalysts in cyberspace have proven to be unsuccessful thus far. This study fills this gap by exploring N2 dissociation on a supported Ru nanoparticle as an example using a universal neural network potential. We calculated 200 catalyst configurations considering the reduction of the support and strong metal-support interaction (SMSI), eventually performing 15,600 calculations for various N2 adsorption states. After successfully validating our results with experimental IR spectral data, we clarified key N2 dissociation pathways behind the high activity of the SMSI surface and disclosed the maximum activity of catalysts reduced at 650 {\deg}C. Our method is well applicable to other complex systems, and we believe it represents a key first step toward the digital transformation of investigations on heterogeneous catalysis.
@article{arxiv.2208.13385,
title = {Cyber Catalysis: N$_2$ Dissociation over Ruthenium Catalyst with Strong Metal-Support Interaction},
author = {Gerardo Valadez Huerta and Kaoru Hisama and Katsutoshi Sato and Katsutoshi Nagaoka and Michihisa Koyama},
journal= {arXiv preprint arXiv:2208.13385},
year = {2022}
}