Predicting nucleation using machine learning in the Ising model

We use a convolutional neural network (CNN) and two logistic regression models to predict the probability of nucleation in the two-dimensional Ising model. The three methods successfully predict the probability for the nearest-neighbor Ising model for which classical nucleation is observed. The CNN outperforms the logistic regression models near the spinodal of the long-range Ising model, but the accuracy of its predictions decreases as the quenches approach the spinodal. An occlusion analysis suggests that this decrease is due to the vanishing difference between the density of the nucleating droplet and the background. Our results are consistent with the general conclusion that predictability decreases near a critical point.