Identification of phase transtitions in molecular systems using unsupervised machine learning methods

Recent advancments in data science and machine learning provide interesting new avenues for studying physical systems. Pattern recognition is a distinct advantage of using machine learning for data analysis. This feature can be leveraged in order to study phase transitions in molecular systems by assuming that a phase transition is accompanied by a change in structure, which is valid for many systems. For example, molecular dynamics (MD) simulations that encapsulate phase transitions provide data for calculating structure information at different temperatures. Various dimensionality reduction methods can be used to isolate the most statistically relevant features of of the structure information in the sample space. From there, clustering methods allow for partitioning of the structure information into different groups based on a similarity criterion. These results can be used in combination with the MD temperature information to predict a transition temperature. The advantage of this approach is that it rerquires minimal a priori information about the system and very little parameter tuning. For systems that are not yet well-understood, such as high entropy alloys, this is an important advantage.