Beyond Local Structures In Critical Supercooled Water Through Unsupervised Learning

• Published in: arXiv.org
• Main Authors: Edward Danquah Donkor, Offei-Danso, Adu, Rodriguez, Alex, Sciortino, Francesco, Ali, Hassanali
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 29.03.2024
• Subjects: Critical point; Density; Heterogeneity; Machine learning; Order parameters; Origins; Physics - Chemical Physics; Physics - Data Analysis, Statistics and Probability; Physics - Soft Condensed Matter; Unsupervised learning; Water; Water chemistry
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2401.16245

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.