Beyond Local Structures In Critical Supercooled Water Through Unsupervised Learning
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866911834795147264 |
|---|---|
| author | Donkor, Edward Danquah Offei-Danso, Adu Rodriguez, Alex Sciortino, Francesco Hassanali, Ali |
| author_facet | Donkor, Edward Danquah Offei-Danso, Adu Rodriguez, Alex Sciortino, Francesco Hassanali, Ali |
| contents | The presence of a second critical point in water has been a topic of intense investigation for the last few decades. The molecular origins underlying this phenomenon are typically rationalized in terms of the competition between local high-density (HD) and low-density (LD) structures. Their identification often require designing parameters that are subject to human intervention. Herein, we use unsupervised learning to discover structures in atomistic simulations of water close to the Liquid-Liquid Critical point (LLCP). Encoding the information of the environment using local descriptors, we do not find evidence for two distinct thermodynamic structures. In contrast, when we deploy non-local descriptors that probe instead heterogeneities on the nanometer length scale, this leads to the emergence of LD and HD domains rationalizing the microscopic origins of the density fluctuations close to criticality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_16245 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Beyond Local Structures In Critical Supercooled Water Through Unsupervised Learning Donkor, Edward Danquah Offei-Danso, Adu Rodriguez, Alex Sciortino, Francesco Hassanali, Ali Soft Condensed Matter Chemical Physics Data Analysis, Statistics and Probability The presence of a second critical point in water has been a topic of intense investigation for the last few decades. The molecular origins underlying this phenomenon are typically rationalized in terms of the competition between local high-density (HD) and low-density (LD) structures. Their identification often require designing parameters that are subject to human intervention. Herein, we use unsupervised learning to discover structures in atomistic simulations of water close to the Liquid-Liquid Critical point (LLCP). Encoding the information of the environment using local descriptors, we do not find evidence for two distinct thermodynamic structures. In contrast, when we deploy non-local descriptors that probe instead heterogeneities on the nanometer length scale, this leads to the emergence of LD and HD domains rationalizing the microscopic origins of the density fluctuations close to criticality. |
| title | Beyond Local Structures In Critical Supercooled Water Through Unsupervised Learning |
| topic | Soft Condensed Matter Chemical Physics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2401.16245 |