Machine learning predictions of surface migration barriers in nucleation and non-equilibrium growth
Experiments and simulations can reveal energetic barriers during atomic-scale growth but are time-consuming. Here, machine learning is applied to single images from kinetic Monte Carlo simulations of sub-monolayer film growth, allowing diffusion barriers and binding energies to be accurately determi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2021-09-01
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| Series: | Communications Materials |
| Online Access: | https://doi.org/10.1038/s43246-021-00188-1 |
| Summary: | Experiments and simulations can reveal energetic barriers during atomic-scale growth but are time-consuming. Here, machine learning is applied to single images from kinetic Monte Carlo simulations of sub-monolayer film growth, allowing diffusion barriers and binding energies to be accurately determined. |
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| ISSN: | 2662-4443 |