Related Experiment Video
Updated: Jun 21, 2025
12:11
Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
Published on: April 8, 2020
8.2K
Gold-Thiolate Nanocluster Dynamics and Intercluster Reactions Enabled by a Machine Learned Interatomic Potential.
Caitlin A McCandler1,2, Antti Pihlajamäki3, Sami Malola3
1Department of Materials Science and Engineering, University of California Berkeley, Berkeley, California 94720, United States.
ACS Nano
|July 10, 2024
Summary
Machine learning potentials enable accurate simulations of gold nanocluster dynamics. New simulations reveal mechanisms for thermal instability and cluster formation in monolayer protected gold nanoclusters.
Area of Science:
- Materials Science
- Computational Chemistry
- Nanotechnology
Background:
- Monolayer protected metal clusters are promising materials, but their solution dynamics are poorly understood.
- Limitations in characterization and modeling hinder the study of prenucleation cluster formation.
- Advancements in machine-learned interatomic potentials offer new avenues for nanoscale simulations.
Purpose of the Study:
- To develop an accurate interatomic potential for simulating thiolate-protected gold nanoclusters.
- To investigate the dynamical behavior and thermal instability of gold nanoclusters in solution.
- To elucidate the mechanisms of cluster formation and ligand exchange.
Main Methods:
- Development of an Au-S-C-H atomic cluster expansion (ACE) interatomic potential.
- Training the potential on over 30,000 density functional theory calculations.
- Performing long molecular dynamics simulations (up to 0.1 μs).
Main Results:
- The ACE potential achieves ab initio accuracy for energies and forces.
- Simulations replicate nanocluster dynamics, including thermal vibration and chiral inversion.
- A mechanism for the thermal instability of neutral Au25(SR)18 clusters was revealed, involving isomerization and chiral forms.
- Cluster coalescence simulations showed ligand exchange mediated by [Au-S] rings.
Conclusions:
- Machine-learned potentials are effective for studying complex nanocluster dynamics.
- The study provides insights into the thermal instability and formation mechanisms of gold nanoclusters.
- This work paves the way for understanding prenucleation phenomena in solution.
