A Self-Evolving Machine-Learning-Based Kinetic Monte Carlo Method for Modelling Thin-Film Growth
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| author | Kimari, Jyri Djurabekova, Flyura Sarakinos, Kostas |
| author_facet | Kimari, Jyri Djurabekova, Flyura Sarakinos, Kostas |
| contents | We present a kinetic Monte Carlo (KMC) simulation framework parameterized by automatically sampling machine-learning (ML) for modeling thin-film growth atom by atom. Given an interatomic potential energy function, the KMC algorithm builds an ML-based regression model for rate parameters on runtime, being trained on the local atomic environments encountered during the system evolution. New environments are continuously added to the training set in a self-evolving manner at points where the ML model estimates high uncertainty. As the simulation progresses, the ML model gains confidence, and the quick estimation of rates increasingly overtakes the relatively-expensive nudged elastic band calculations, promoting computational efficiency while retaining high fidelity description of the atomic diffusion kinetics. As a test case, we simulate the sub-monolayer growth of Ag on Ag {111}, where we demonstrate adatom islands forming in shapes and densities in accordance with the underlying atomistic interaction model, the theoretical framework, and available experimental results related to thin-film nucleation and growth. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_31144 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Self-Evolving Machine-Learning-Based Kinetic Monte Carlo Method for Modelling Thin-Film Growth Kimari, Jyri Djurabekova, Flyura Sarakinos, Kostas Materials Science We present a kinetic Monte Carlo (KMC) simulation framework parameterized by automatically sampling machine-learning (ML) for modeling thin-film growth atom by atom. Given an interatomic potential energy function, the KMC algorithm builds an ML-based regression model for rate parameters on runtime, being trained on the local atomic environments encountered during the system evolution. New environments are continuously added to the training set in a self-evolving manner at points where the ML model estimates high uncertainty. As the simulation progresses, the ML model gains confidence, and the quick estimation of rates increasingly overtakes the relatively-expensive nudged elastic band calculations, promoting computational efficiency while retaining high fidelity description of the atomic diffusion kinetics. As a test case, we simulate the sub-monolayer growth of Ag on Ag {111}, where we demonstrate adatom islands forming in shapes and densities in accordance with the underlying atomistic interaction model, the theoretical framework, and available experimental results related to thin-film nucleation and growth. |
| title | A Self-Evolving Machine-Learning-Based Kinetic Monte Carlo Method for Modelling Thin-Film Growth |
| topic | Materials Science |
| url | https://arxiv.org/abs/2605.31144 |