Fang Sun, Qing Tang
Atomically precise silver nanoclusters (NCs) protected by alkynyl ligands represent an emerging class of electrocatalysts demonstrating high activity and selectivity in reactions, such as CO 2 electroreduction. However, their dynamic structural evolution mechanisms under electrochemical operating conditions remain elusive. Conventional experimental characterization faces a grand challenge to resolve atomic-scale dynamic processes, while ab initio molecular dynamics (AIMD) simulations are solely confined to picosecond time scales, insufficient for capturing the dynamics of evolution over longer times. Combining multiscale constant potential simulations and a deep potential molecular dynamics (DPMD) scheme, here we developed a high-accuracy machine learning force field within the deep-learning framework to elucidate the electrochemical structural evolution in all-alkynyl-protected Ag 15 NC and its doped systems (Ag 8 Au 7, Ag 9 Cu 6, and Ag 14 Cl NCs). We found that the metal cores of all NCs undergo a transition from octahedral to disordered, accompanied by partial or complete cleavage of surface alkynyl ligands. The dopants critically modulate the stability by regulating desorption pathways, with Ag 9 Cu 6 NC exhibiting exceptional resistance to dissociation due to robust Cu–C bonding. Our nanosecond-level DPMD simulations based on trained machine learning force fields further confirmed that doping dramatically affects the number of desorbed alkyne ligands (4 for Ag 15, 6 for Ag 8 Au 7, and 8 for Ag 14 Cl) and the degree of core ordering, and a long-term simulation of >2000 ps was crucial for capturing the dynamic electrochemical interface. This study established the first quantitative correlation between electrochemical interface dynamics and doping effects, providing a theoretical paradigm for designing highly stable atomically precise catalysts.