  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.
