Simulating Crystallization in a Colloidal System Using State Predictive Information Bottleneck based Enhanced Sampling
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866916226824929280 |
|---|---|
| author | Meraz, Vanessa J. Zou, Ziyue Tiwary, Pratyush |
| author_facet | Meraz, Vanessa J. Zou, Ziyue Tiwary, Pratyush |
| contents | We investigate crystal nucleation in supersaturated colloid suspensions using enhanced molecular dynamics simulations augmented with machine learning techniques. The simulations reveal that crystallization in the model colloidal system studied here, with particles interacting through a repulsive screened Coulomb Yukawa potential, proceeds from vapor to dense liquid droplet to crystalline phases across multiple high barriers. Employing a one-dimensional reaction coordinate derived from the State Predictive Information Bottleneck framework, our simulations capture backand-forth phase transitions across multiple barriers effectively in biased metadynamics simulations. We obtain relative free energy differences between different phases and also quantify the roles of different molecular level features in driving the phase changes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_17722 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Simulating Crystallization in a Colloidal System Using State Predictive Information Bottleneck based Enhanced Sampling Meraz, Vanessa J. Zou, Ziyue Tiwary, Pratyush Soft Condensed Matter Materials Science We investigate crystal nucleation in supersaturated colloid suspensions using enhanced molecular dynamics simulations augmented with machine learning techniques. The simulations reveal that crystallization in the model colloidal system studied here, with particles interacting through a repulsive screened Coulomb Yukawa potential, proceeds from vapor to dense liquid droplet to crystalline phases across multiple high barriers. Employing a one-dimensional reaction coordinate derived from the State Predictive Information Bottleneck framework, our simulations capture backand-forth phase transitions across multiple barriers effectively in biased metadynamics simulations. We obtain relative free energy differences between different phases and also quantify the roles of different molecular level features in driving the phase changes. |
| title | Simulating Crystallization in a Colloidal System Using State Predictive Information Bottleneck based Enhanced Sampling |
| topic | Soft Condensed Matter Materials Science |
| url | https://arxiv.org/abs/2404.17722 |