用机器学习预测Ising模型中旋转线附近的成核(CS LG)---Elva

我们利用卷积神经网络（CNN）和两个逻辑斯蒂回归模型预测了二维Ising模型中的成核现象。CNN在具有不同交互范围和大小的系统中优于后者，特别是当系统的大小变大时。我们发现，随着系统向旋转线越来越近，CNN的预测能力降低。在本文中，我们用旋节形核理论预测的枝晶液滴结构给出了解释。

原文题目：Predicting nucleation near the spinodal in the Ising model using machine learning

原文：We predict the occurrence of nucleation in the two-dimensional Ising model using the Convolutional Neural Network (CNN) and two logistic regression models. CNN outperforms the latter in systems with different interaction ranges and sizes, especially when the size of the system becomes large. We find that the CNN decreases its prediction power as system gets closer to the spinodal. We give explanation using the ramified droplet structure predicted by spinodal nucleation theory.

原文作者：Shan Huang, William Klein, Harvey Gould

原文地址：https://arxiv.org/abs/2004.09575