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

Ogrin, Peter (avtor), Urbič, Tomaž (avtor)

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.

• Jezik: Angleški jezik
• Ključne besede: algorithms, distribution function, molecules, phase diagrams, phase transitions
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FKKT - Fakulteta za kemijo in kemijsko tehnologijo
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2025
• Št. strani: Str. 3867–3887
• Številčenje: Vol. 21, iss. 8
• PID: 20.500.12556/RUL-168757
• UDK: 544.344.015.3:004.85
• ISSN pri članku: 1549-9618
• DOI: 10.1021/acs.jctc.4c01456
• COBISS.SI-ID: 233011459
• Datum objave v RUL: 23.04.2025

Gradivo je del revije

• Naslov: Journal of chemical theory and computation
• Skrajšan naslov: J. chem. theory comput.
• Založnik: American Chemical Society
• ISSN: 1549-9618
• COBISS.SI-ID: 26256901

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: fazni diagrami, modeli vode, strojno učenje