kMC simulations for publication "Control of Growth Morphology of Deposited fcc Metals through Tuning Substrate–Metal Interactions"

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 ChipsJoint Undertaking, through the European Union’s DigitalEurope (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 📧 [e-mail]