Thermal dynamics and coalescence of Au144(SR)60 clusters from a machine-learned potential

This data repository contains molecular dynamics trajectory files associated with the article: https://www.nature.com/articles/s41467-025-67700-w
“Thermal dynamics and coalescence of Au₁₄₄(SR)₆₀ clusters from a machine‑learned potential.”
The simulations were performed using a machine‑learned (ML) interatomic potential trained on ab initio reference data and executed within the LAMMPS molecular dynamics package.
Each simulation captures the structural and thermal evolution of Au₁₄₄(SR)₆₀ nanoclusters under different thermal conditions, providing comprehensive insight into both single‑cluster behavior and cluster–cluster coalescence mechanisms.
The root directory, **Au144SR60_ML_dynamics_coalescence**, contains two principal subfolders: `Single_cluster_dynamics` and `Cluster_Cluster_interaction_dynamics`.

Single_cluster_dynamics, holds independent simulations of a single Au₁₄₄(SR)₆₀ cluster for six temperatures (300 K, 325 K, 350 K, 375 K, 400 K, and 450 K).
For each temperature, four replicas were performed for 120 ns, organized into subfolders named `1st`, `2nd`, `3rd`, and `4th`.
Each replica folder contains one trajectory/velocities file named following the convention `[replica_number]_velocities_[temperature]_120ns.lammpstrj`

The root directory, *Cluster_Cluster_interaction_dynamics*, contains trajectories describing the interaction of two Au₁₄₄(SR)₆₀ clusters at selected temperatures and initial separations. Within this folder, the subdirectory `400K_3.1Ang` provides the coalescence trajectory at 400 K with an initial inter‑cluster distance of 3.1 Å, and
`550K_1.6Ang` includes the corresponding simulation at 550 K and 1.6 Å initial separation. In each case, the files named as: `velocities_Au144Au144_[temperature]_[distance].lammpstrj`. The trajectories can be visualized with VMD (Visual Molecular Dynamics) to observe atomic motion, melting transitions, and coalescence processes.

Together, these datasets provide a comprehensive reference for the finite‑temperature stability and fusion behavior of Au₁₄₄(SR)₆₀ nanoclusters derived from a ML interatomic potential. If you use these trajectories or results in your own work, please cite the corresponding publication: *“Thermal dynamics and coalescence of Au₁₄₄(SR)₆₀ clusters from a machine‑learned potential.”* For further information or questions about the dataset, please contact Dr. Maryam Sabooni Asre Hazer: [e-mail], or Prof. Hannu Häkkinen: [e-mail] Department of Physics, Nanoscience center, University of Jyväskylä.