Calculating a phase diagram of a simple water model using unsupervised machine learning on simulation data

Ogrin, Peter (author), Urbič, Tomaž (author)

We use unsupervised machine learning to construct a phase diagram of a simple 2D rose water model. The machine learning method that we use is a combination of dimensionality reduction methods and clustering algorithms. Two different data sets from the same simulations are used as input data for machine learning. These are angular distribution functions and a set of different thermodynamic, dynamic, and structural properties. To evaluate the efficiency of the method, the machine learning results are compared to manually determined phase diagrams. We show that the methods successfully predict the phase diagram of the rose water model. Furthermore, the phase diagrams obtained from the two data sets are in semiquantitative agreement with each other. Four different solid phases, one liquid phase, and one gaseous phase were determined. The method we have presented is straightforward and easy to implement. It requires almost no prior knowledge of the system to obtain a phase diagram. The method can also be used to distinguish between the different parts of the same phase that have different properties or a sufficiently different structure, and in this way find local differences and anomalies.

• Language: English
• Keywords: algorithms, distribution function, molecules, phase diagrams, phase transitions
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FKKT - Faculty of Chemistry and Chemical Technology
• Publication status: Published
• Publication version: Version of Record
• Year: 2025
• Number of pages: Str. 3867–3887
• Numbering: Vol. 21, iss. 8
• PID: 20.500.12556/RUL-168757
• UDC: 544.344.015.3:004.85
• ISSN on article: 1549-9618
• DOI: 10.1021/acs.jctc.4c01456
• COBISS.SI-ID: 233011459
• Publication date in RUL: 23.04.2025

Record is a part of a journal

• Title: Journal of chemical theory and computation
• Shortened title: J. chem. theory comput.
• Publisher: American Chemical Society
• ISSN: 1549-9618
• COBISS.SI-ID: 26256901

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: fazni diagrami, modeli vode, strojno učenje