论文标题
二维复合物等离子体的相变研究中的机器学习
Machine learning in the study of phase transition of two-dimensional complex plasmas
论文作者
论文摘要
机器学习用于研究二维复合物等离子体的相变。使用Langevin动力学模拟来制备各种热力学状态的颗粒悬浮液。基于在两个极端条件下所产生的粒子位置，将位图图像合成并导入卷积神经网络（Convnet）作为训练样本。结果，获得了相图。该训练有素的Convnet模型可以直接应用于实验中的视频显微镜以研究熔化的录制图像序列。
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.
