论文标题
使用机器学习预测ISING模型中旋转的成核
Predicting nucleation near the spinodal in the Ising model using machine learning
论文作者
论文摘要
我们使用卷积神经网络（CNN）和两个逻辑回归模型来预测二维ISING模型中成核的概率。这三个模型成功地预测了观察到经典成核的最接近邻居Ising模型的概率。 CNN的表现优于远程模型旋转模型附近的逻辑回归模型，但是随着淬灭接近旋转的速度，其预测的准确性降低。遮挡分析表明，这种减少是由于成核液滴和背景密度消失的差异所致。我们的结果与总体结论一致，即可预测性降低接近临界点。
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 models 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. 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.
