Ogrin Peter, Urbic Tomaz
Faculty of Chemistry and Chemical Technology, University of Ljubljana, Vecna Pot 113, SI-1000 ljubljana, Slovenia.
J Chem Theory Comput. 2025 Apr 22;21(8):3867-3887. doi: 10.1021/acs.jctc.4c01456. Epub 2025 Apr 14.
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.
我们使用无监督机器学习来构建一个简单二维玫瑰水模型的相图。我们所使用的机器学习方法是降维方法与聚类算法的结合。来自相同模拟的两个不同数据集被用作机器学习的输入数据。这些是角分布函数以及一组不同的热力学、动力学和结构性质。为了评估该方法的效率，将机器学习结果与人工确定的相图进行比较。我们表明这些方法成功地预测了玫瑰水模型的相图。此外，从两个数据集获得的相图彼此半定量一致。确定了四种不同的固相、一种液相和一种气相。我们提出的方法直接且易于实现。几乎不需要关于该系统的先验知识就能获得相图。该方法还可用于区分具有不同性质或足够不同结构的同一相的不同部分，从而发现局部差异和异常。
