Rare Event Sampling using Smooth Basin Classification
Fuente:
arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913360685039616 |
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| author | Vandenhaute, Sander Braeckevelt, Tom Dobbelaere, Pieter Bocus, Massimo Van Speybroeck, Veronique |
| author_facet | Vandenhaute, Sander Braeckevelt, Tom Dobbelaere, Pieter Bocus, Massimo Van Speybroeck, Veronique |
| contents | The efficiency of atomic simulations of materials and molecules can rapidly deteriorate when large free energy barriers exist between local minima. We propose smooth basin classification, a universal method to define reaction coordinates based on the internal feature representation of a graph neural network. We achieve high data efficiency by exploiting their built-in symmetry and adopting a transfer learning strategy. We benchmark our approach on challenging chemical and physical transformations, and show that it matches and even outperforms reaction coordinates defined based on human intuition. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_03777 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Rare Event Sampling using Smooth Basin Classification Vandenhaute, Sander Braeckevelt, Tom Dobbelaere, Pieter Bocus, Massimo Van Speybroeck, Veronique Chemical Physics Statistical Mechanics The efficiency of atomic simulations of materials and molecules can rapidly deteriorate when large free energy barriers exist between local minima. We propose smooth basin classification, a universal method to define reaction coordinates based on the internal feature representation of a graph neural network. We achieve high data efficiency by exploiting their built-in symmetry and adopting a transfer learning strategy. We benchmark our approach on challenging chemical and physical transformations, and show that it matches and even outperforms reaction coordinates defined based on human intuition. |
| title | Rare Event Sampling using Smooth Basin Classification |
| topic | Chemical Physics Statistical Mechanics |
| url | https://arxiv.org/abs/2404.03777 |