标题： 基于机器学习的潜热估算 显示英文标题

标题： Latent heat estimation with machine learning

摘要： 我们着手探讨利用深度机器学习研究具有一阶相变系统的临界行为的可能性。 我们提出了一种基于已知无序相能量和有序相能量的瞬时自旋构型三元分类的机器学习协议。 训练好的神经网络用于预测给定样本属于某一相还是另一相。 这使我们可以估计具有特定能量的构型属于有序相、混合相和无序相的概率。 从这些概率中，我们得到了具有10个和20个分量的Potts模型（经历强不连续转变）的临界能量和潜热的估值。 我们还发现，这些概率可以反映混合相中的几何转变。 显示英文摘要

摘要： We set out to explore the possibility of investigating the critical behavior of systems with first-order phase transition using deep machine learning. We propose a machine learning protocol with ternary classification of instantaneous spin configurations using known values of disordered phase energy and ordered phase energy. The trained neural network is used to predict whether a given sample belong to one or the other phase of matter. This allows us to estimate the probability that configurations with a certain energy belong to the ordered phase, mixed phase and, disordered phase. From these probabilities, we obtained estimates of the values of the critical energies and the latent heat for the Potts model with 10 and 20 components, which undergoes a strong discontinuous transition. We also find that the probabilities can reflect geometric transitions in the mixed phase.