Pd、Pt、Cu、Ag、Auナノクラスター上での普遍的ニューラルネットワークポテンシャルを用いた触媒的H₂/O₂再結合の分子動力学研究

The H₂/O₂ 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 H₂/O₂ 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 H₂O 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 H₂/O₂ across the studied metals, demonstrating the time evolution of gas-surface interactions and the overall detailed reaction mechanism of the H₂/O₂ recombination reaction. In particular, the reaction pathway O₂ → OOH → H₂O 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 O₂ dissociation. The two features of molecular O₂ 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 H₂/O₂ recombination reaction, offering a fast and accurate workflow for both performance screening and mechanism discovery, thus accelerating catalyst design.