kMC simulations for publication "Control of Growth Morphology of Deposited fcc Metals through Tuning Substrate–Metal Interactions"
Authors/Creators
Description
Kinetic Monte Carlo Simulations of FCC Metal Growth
Metals: Ag, Au, Cu, Ni, Pd, Pt
Growth direction: (111)
Processes: Homoepitaxial growth, heteroepitaxial growth (via substrate interaction tuning), and post-deposition thermal vacuum annealing
📄 Overview
This repository and dataset accompany the manuscript, containing the source code, input parameters, simulation outputs, and analysis scripts required to reproduce the results:
"Control of Growth Morphology of Deposited fcc Metals through Tuning Substrate–Metal Interactions"
Authors: Samuel Aldana Delgado, Michael Nolan
Journal: ACS Applied Materials & Interfaces
- DOI: [10.1021/acsami.5c18081]
- arXiv: [arXiv.2508.21492]
The simulations utilize an open-source kinetic Monte Carlo simulator (kMC) simulator developed in Python by Dr. Samuel Aldana Delgado, designed to model thin-film growth and thermal vacuum annealing dynamics.
- Code repository: [github.com/aldanads/Control-of-growth-morphology...]
This dataset is structured to support reproducibility, multiscale modeling, and machine learning applications in nanofabrication and materials design.
📂 Directory Structure
├── outputs/ # Primary simulation output directory
│ └── Metal # e.g., Ag, Au, Cu, Ni, Pd, Pt
│ └── Experiment_Type # e.g., annealing, homoepitaxial, Substrate_range_downward_v2 (heteroepitaxial growth)
│ └── Sim_N/
│ ├── Crystal_evolution/
│ │ ├── *.dump # Subsampled atomic trajectories (LAMMPS format)
│ │ ├── metadata.json # Raw simulation metadata (original, v1)
│ │ ├── metadata_v2.json # Enhanced, standardized metadata
│ │ └── results_summary.json # Key post-processed observables (ML-ready)
│ └── Program/
│ ├── *.py # Exact code version used
│ └── variables.pkl # Final system state (pickled)
│
├── Figures/
│ ├── *.ipynb # Jupyter notebooks to reproduce manuscript figures
│ └── Processed_data/
│ │ ├── *.csv # files with processed data
│
├── manuscript/
│ ├── manuscript_preprint.pdf
│ └── Supporting_information/ # Additional validation data and methods
🔑 Key Files for Reuse
| File | Purpose | Format |
| metadata_v2.json | Standardized simulation parameters (domain size, process type) | JSON |
| results_summary.json | ML-ready targets: roughness, coverage, island density, aspect ratio, etc. | JSON |
| *.dump | Atomic trajectories (subsampled) | LAMMPS dump |
| `Figure.csv | Summary table linking simulations to high-level metrics | CSV |
📜 Licensing
- Simulation code: MIT License
- Dataset (metadata, results, trajectories): CC BY 4.0
→ You are free to share and adapt the data, provided you give appropriate credit.
🙏 Acknowledgments
The acquisition and operation are jointly funded by the Chips
Joint Undertaking, through the European Union’s Digital
Europe (101183266) and Horizon Europe programs
(101183277), as well as by the participating states Belgium
(Flanders), France, Germany, Finland, Ireland and Romania.
📬 Contact
For questions or collaboration:
Dr. Samuel Aldana Delgado
Tyndall National Institute / University College Cork
📧 samuel.delgado@tyndall.ie
Files
Control of fcc metal morphology via substrate interaction.pdf
Additional details
Identifiers
- DOI
- 10.1021/acsami.5c18081
- arXiv
- arXiv:2508.21492v2
Software
- Repository URL
- https://github.com/aldanads/Control-of-growth-morphology-of-deposited-fcc-metals-through-tuning-substrate-metal-interactions
- Programming language
- Python
- Development Status
- Active