Machine learning in the study of phase transition of two-dimensional complex plasmas

Abstract

Machine learning is applied to investigate the phase transition of two-dimensional complex plasmas. The Langevin dynamics simulation is employed to prepare particle suspensions in various thermodynamic states. Based on the resulted particle positions in two extreme conditions, bitmap images are synthesized and imported to a convolutional neural network (ConvNet) as a training sample. As a result, a phase diagram is obtained. This trained ConvNet model has been directly applied to the sequence of the recorded images using video microscopy in the experiments to study the melting.