Machine learning has recently been applied to many problems in condensed matter physics. A common point of many proposals is to save computational cost by training the machine with data from a simple example and then using the machine to make predictions for a more complicated example. Convolutional neural networks (CNN), which are one of the tools of machine learning, have proved to work well for assessing eigenfunctions in disordered systems. Here we apply a CNN to assess Kohn–Sham eigenfunctions obtained in density functional theory (DFT) simulations of the metal–insulator transition of a doped semiconductor. We demonstrate that a CNN that has been trained using eigenfunctions from a simulation of a doped semiconductor that neglects electron spin successfully predicts the critical concentration when presented with eigenfunctions from simulations that include spin.

中文翻译：

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在掺杂半导体中金属-绝缘体转变的模拟中使用卷积神经网络分析 Kohn-Sham 特征函数

机器学习最近被应用于凝聚态物理中的许多问题。许多提议的一个共同点是通过使用来自简单示例的数据训练机器，然后使用机器对更复杂的示例进行预测来节省计算成本。卷积神经网络 (CNN) 是机器学习的工具之一，已被证明可以很好地评估无序系统中的特征函数。在这里，我们应用 CNN 来评估在掺杂半导体的金属 - 绝缘体转变的密度泛函理论 (DFT) 模拟中获得的 Kohn-Sham 特征函数。