Abstract
The H(2)/O(2) catalytic recombination reaction, essential for safely utilizing hydrogen energy, was simulated rapidly and accurately using the molecular dynamics (MD) simulation with a machine-learning potential known as the 'universal neural network potential' (UNNP). The catalytic activities of Pd, Pt, Cu, Ag, and Au nanoclusters were systematically investigated under a 100 bar H(2)/O(2) atmosphere at 500 K. This approach enables the investigation of the entirely dynamic catalytic reaction, rather than assembling a static configuration at 0 K. Throughout the simulation, the ability to catalyze H(2)O formation on the Pt surface was successfully reproduced, aligning with previous experimental findings. The simulations have revealed characteristic differences and the suitability of reactivity with H(2)/O(2) across the studied metals, demonstrating the time evolution of gas-surface interactions and the overall detailed reaction mechanism of the H(2)/O(2) recombination reaction. In particular, the reaction pathway O(2) → OOH → H(2)O was revealed to occur preferentially on the Pt nanocluster, while other metals exhibited unsuitability, such as Pd's intense H trapping and Cu's surface instability due to excessive O(2) dissociation. The two features of molecular O(2) adsorption and the facile surface diffusion of H atoms play a crucial role as necessary conditions for catalytic activity. The accelerated MD method enables the rapid and accurate elucidation of the atomic-level mechanisms and activity differences in the H(2)/O(2) recombination reaction, offering a fast and accurate workflow for both performance screening and mechanism discovery, thus accelerating catalyst design.