Machine learning in the study of phase transition of two-dimensional complex plasmas
Plasma Physics
2023-07-25 v1
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 training sample. As a result, a phase diagram is obtained. This trained ConvNet model can be directly applied to the sequence of the recorded images using video microscopy in the experiments to study the melting.
Keywords
Cite
@article{arxiv.2204.12702,
title = {Machine learning in the study of phase transition of two-dimensional complex plasmas},
author = {He Huang and Vladimir Nosenko and Han-Xiao Huang-Fu and Hubertus M. Thomas and Cheng-Ran Du},
journal= {arXiv preprint arXiv:2204.12702},
year = {2023}
}