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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.05528 |
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| _version_ | 1866916643709386752 |
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| author | Pabst, Florian Baroni, Stefano |
| author_facet | Pabst, Florian Baroni, Stefano |
| contents | The microscopic understanding of the dramatic increase in viscosity of liquids when cooled towards the glass transition is a major unresolved issue in condensed matter physics. Here, we use machine learning methods to accelerate molecular dynamics simulations with first-principles accuracy for the glass-former toluene. We show that the increase in viscosity is intimately linked to the increasing number of dynamically correlated molecules $N^*$. While certain hallmark features of glassy dynamics, like physical aging, are linked to $N^*$ as well, others, like relaxation stretching, are not. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_05528 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Glassy Dynamics from First-Principles Simulations Pabst, Florian Baroni, Stefano Disordered Systems and Neural Networks The microscopic understanding of the dramatic increase in viscosity of liquids when cooled towards the glass transition is a major unresolved issue in condensed matter physics. Here, we use machine learning methods to accelerate molecular dynamics simulations with first-principles accuracy for the glass-former toluene. We show that the increase in viscosity is intimately linked to the increasing number of dynamically correlated molecules $N^*$. While certain hallmark features of glassy dynamics, like physical aging, are linked to $N^*$ as well, others, like relaxation stretching, are not. |
| title | Glassy Dynamics from First-Principles Simulations |
| topic | Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2408.05528 |