Capturing Dynamic Core Reconstruction and Ligand Desorption of Atomically Precise Ag Nanoclusters with Machine...

Area of Science:

• Materials Science
• Electrochemistry
• Computational Chemistry

Background:

• Atomically precise silver nanoclusters (NCs) protected by alkynyl ligands show potential as electrocatalysts for CO2 electroreduction.
• Understanding the dynamic structural evolution of these NCs under electrochemical conditions is crucial but challenging due to limitations in experimental and traditional simulation techniques.

Purpose of the Study:

• To elucidate the electrochemical structural evolution mechanisms of alkynyl-protected Ag15 NCs and their doped systems (Ag8Au7, Ag9Cu6, Ag14Cl).
• To establish a quantitative correlation between electrochemical interface dynamics and doping effects for designing stable atomically precise catalysts.

Main Methods:

• Developed a high-accuracy machine learning force field using a deep potential molecular dynamics (DPMD) scheme combined with multiscale constant potential simulations.
• Performed nanosecond-level DPMD simulations to capture atomic-scale dynamic processes and electrochemical interface evolution.

Main Results:

• Observed a transition from octahedral to disordered metal cores in all NCs, with partial or complete alkynyl ligand cleavage.
• Dopants modulate NC stability by regulating ligand desorption pathways; Ag9Cu6 NC showed superior resistance due to robust Cu-C bonding.
• Doping significantly affects the number of desorbed ligands and core ordering, with long-term simulations crucial for capturing interface dynamics.

Conclusions:

• The study provides the first quantitative correlation between electrochemical interface dynamics and doping effects in atomically precise nanoclusters.
• The findings offer a theoretical framework for designing highly stable and active atomically precise electrocatalysts through strategic doping.