20.500.12556/RUL-168757 Calculating a phase diagram of a simple water model using unsupervised machine learning on simulation data 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. algorithms distribution function molecules phase diagrams phase transitions fazni diagrami modeli vode strojno učenje true false true Angleški jezik Slovenski jezik Članek v reviji 2025-04-23 13:07:42 2025-04-23 13:07:43 2025-04-24 04:03:33 0000-00-00 00:00:00 2025 0 0 Str. 3867–3887 iss. 8 Vol. 21 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 544.344.015.3:004.85 1549-9618 10.1021/acs.jctc.4c01456 233011459 ogrin-urbic-2025-calculating-a-phase-diagram-of-a-simple-water-model-using-unsupervised-machine-learning-on-simulation.pdf ogrin-urbic-2025-calculating-a-phase-diagram-of-a-simple-water-model-using-unsupervised-machine-learning-on-simulation.pdf 1 20238478EE84364AE4769380FB19678A 90d75a0e202ae9824194fcf6f20c2c363f9a48768f4bd67e96ec9f2e7c167ad0 73c4d1de-2032-11f0-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=209676 https://pubs.acs.org/doi/10.1021/acs.jctc.4c01456 1 09560a7f-2032-11f0-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=209675 Fakulteta za kemijo in kemijsko tehnologijo 0 0 0