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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 N$_2$ 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 N$_2$ adsorption states. After successfully validating our results with experimental IR spectral data, we clarified key N$_2$ dissociation pathways behind the high activity of the SMSI surface and disclosed the maximum activity of catalysts reduced at 650 °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.