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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.01118 |
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| _version_ | 1866912357783961600 |
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| author | Natarajan, S. Kondati Schneider, J. Pandey, N. Wellendorff, J. Smidstrup, S. |
| author_facet | Natarajan, S. Kondati Schneider, J. Pandey, N. Wellendorff, J. Smidstrup, S. |
| contents | Atomistic modeling of thin-film processes provides an avenue not only for discovering key chemical mechanisms of the processes but also to extract quantitative metrics on the events and reactions taking place at the gas-surface interface. Molecular dynamics (MD) is a powerful computational method to study the evolution of a process at the atomic scale, but studies of industrially relevant processes usually require suitable force fields, which are in general not available for all processes of interest. However, machine learned force fields (MLFF) are conquering the field of computational materials and surface science. In this paper, we demonstrate how to efficiently build MLFFs suitable for process simulations and provide two examples for technologically relevant processes: precursor pulse in the atomic layer deposition of HfO2 and atomic layer etching of MoS2. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_01118 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On Simulating Thin-Film Processes at the Atomic Scale Using Machine Learned Force Fields Natarajan, S. Kondati Schneider, J. Pandey, N. Wellendorff, J. Smidstrup, S. Materials Science Machine Learning Atomistic modeling of thin-film processes provides an avenue not only for discovering key chemical mechanisms of the processes but also to extract quantitative metrics on the events and reactions taking place at the gas-surface interface. Molecular dynamics (MD) is a powerful computational method to study the evolution of a process at the atomic scale, but studies of industrially relevant processes usually require suitable force fields, which are in general not available for all processes of interest. However, machine learned force fields (MLFF) are conquering the field of computational materials and surface science. In this paper, we demonstrate how to efficiently build MLFFs suitable for process simulations and provide two examples for technologically relevant processes: precursor pulse in the atomic layer deposition of HfO2 and atomic layer etching of MoS2. |
| title | On Simulating Thin-Film Processes at the Atomic Scale Using Machine Learned Force Fields |
| topic | Materials Science Machine Learning |
| url | https://arxiv.org/abs/2505.01118 |