Structure and polymerization of liquid sulfur across the $λ$-transition
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914361866452992 |
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| author | Yang, Manyi Trizio, Enrico Parrinello, Michele |
| author_facet | Yang, Manyi Trizio, Enrico Parrinello, Michele |
| contents | The anomalous $λ$-transition of liquid sulfur, which is supposed to be related to the transformation of eight-membered sulfur rings into long polymeric chains, has attracted considerable attention. However, a detailed description of the underlying dynamical polymerization process is still missing. Here, we study the structures and the mechanism of the polymerization processes of liquid sulfur across the $λ$-transition as well as its reverse process of formation of the rings. We do so by performing ab-initio-quality molecular dynamics simulations thanks to a combination of machine learning potentials and state-of-the-art enhanced sampling techniques. With our approach, we obtain structural results that are in good agreement with the experiments and we report precious dynamical insights into the mechanisms involved in the process. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_04572 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Structure and polymerization of liquid sulfur across the $λ$-transition Yang, Manyi Trizio, Enrico Parrinello, Michele Soft Condensed Matter Computational Physics The anomalous $λ$-transition of liquid sulfur, which is supposed to be related to the transformation of eight-membered sulfur rings into long polymeric chains, has attracted considerable attention. However, a detailed description of the underlying dynamical polymerization process is still missing. Here, we study the structures and the mechanism of the polymerization processes of liquid sulfur across the $λ$-transition as well as its reverse process of formation of the rings. We do so by performing ab-initio-quality molecular dynamics simulations thanks to a combination of machine learning potentials and state-of-the-art enhanced sampling techniques. With our approach, we obtain structural results that are in good agreement with the experiments and we report precious dynamical insights into the mechanisms involved in the process. |
| title | Structure and polymerization of liquid sulfur across the $λ$-transition |
| topic | Soft Condensed Matter Computational Physics |
| url | https://arxiv.org/abs/2311.04572 |