Using Machine Learning to analyze Defect Annihilation Dynamics in Smectic C films

We demonstrate a method for training a convolutional neural network with simulated images for usage in the study of topological defect annihilation in freely-suspended SmC liquid crystal films. Modern machine learning methods require large, robust training data sets to generate accurate predictions. Generating these training sets requires a significant up-front time investment that is often impractical for small-scale applications. Here we demonstrate a ‘full-stack’ computational solution, where the training data set is generated on-the-fly using a noise injection process to produce simulated data characteristic of the experimental system. The experiment requires accurate observations of both the spatial distribution of the defects and the total number of defects at every time step, making it an ideal system for testing the robustness of the trained network. The fully trained network was found to be comparable in accuracy to identifying the defects by hand, with a four-orders of magnitude improvement in time efficiency.