论文标题
易于解释和无监督的阶段分类
Interpretable and unsupervised phase classification
论文作者
论文摘要
完全自动化的分类方法可以直接对相图产生直接的物理见解。在这里，我们展示了一种无监督的机器学习方法，用于相分类，该方法可通过其最佳预测的分析推导来解释，并允许为订单参数提供自动化的构造方案。基于这些发现，我们提出并应用了一种替代性，动机，数据驱动的方案，该方案依赖于平均输入特征之间的差异。这种基于平均值的方法在计算上便宜且直接解释。例如，我们考虑了无旋转的Falicov-Kimball模型的物理富含地面相图。
Fully automated classification methods that yield direct physical insights into phase diagrams are of current interest. Here, we demonstrate an unsupervised machine learning method for phase classification which is rendered interpretable via an analytical derivation of its optimal predictions and allows for an automated construction scheme for order parameters. Based on these findings, we propose and apply an alternative, physically-motivated, data-driven scheme which relies on the difference between mean input features. This mean-based method is computationally cheap and directly interpretable. As an example, we consider the physically rich ground-state phase diagram of the spinless Falicov-Kimball model.
