Tailoring Frictional Properties of Surfaces Using Diffusion Models
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866913191538196480 |
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| author | Nordhagen, Even Marius Sveinsson, Henrik Andersen Malthe-Sørenssen, Anders |
| author_facet | Nordhagen, Even Marius Sveinsson, Henrik Andersen Malthe-Sørenssen, Anders |
| contents | This Letter introduces an approach for precisely designing surface friction properties using a conditional generative machine learning model, specifically a diffusion denoising probabilistic model (DDPM). We created a dataset of synthetic surfaces with frictional properties determined by molecular dynamics simulations, which trained the DDPM to predict surface structures from desired frictional outcomes. Unlike traditional trial-and-error and numerical optimization methods, our approach directly yields surface designs meeting specified frictional criteria with high accuracy and efficiency. This advancement in material surface engineering demonstrates the potential of machine learning in reducing the iterative nature of surface design processes. Our findings not only provide a new pathway for precise surface property tailoring but also suggest broader applications in material science where surface characteristics are critical. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_05206 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Tailoring Frictional Properties of Surfaces Using Diffusion Models Nordhagen, Even Marius Sveinsson, Henrik Andersen Malthe-Sørenssen, Anders Computational Physics Machine Learning This Letter introduces an approach for precisely designing surface friction properties using a conditional generative machine learning model, specifically a diffusion denoising probabilistic model (DDPM). We created a dataset of synthetic surfaces with frictional properties determined by molecular dynamics simulations, which trained the DDPM to predict surface structures from desired frictional outcomes. Unlike traditional trial-and-error and numerical optimization methods, our approach directly yields surface designs meeting specified frictional criteria with high accuracy and efficiency. This advancement in material surface engineering demonstrates the potential of machine learning in reducing the iterative nature of surface design processes. Our findings not only provide a new pathway for precise surface property tailoring but also suggest broader applications in material science where surface characteristics are critical. |
| title | Tailoring Frictional Properties of Surfaces Using Diffusion Models |
| topic | Computational Physics Machine Learning |
| url | https://arxiv.org/abs/2401.05206 |