论文标题
使用卷积神经网络在掺杂的半导体中的金属 - 绝缘体过渡中分析Kohn-Sham本征函数
Analysis of Kohn-Sham Eigenfunctions Using a Convolutional Neural Network in Simulations of the Metal-insulator Transition in Doped Semiconductors
论文作者
论文摘要
机器学习最近已应用于凝结物理学的许多问题。许多建议的共同点是通过用简单示例的数据训练机器来节省计算成本，然后使用机器为一个更复杂的示例做出预测。卷积神经网络（CNN）是机器学习的工具之一，已被证明在评估无序系统中的本征函数方面非常有效。在这里，我们应用CNN评估在掺杂半导体的金属 - 绝缘体过渡中获得的Kohn-Sham征函数。我们证明，使用掺杂半导体的模拟中的征征训练的CNN，忽略电子旋转的掺杂半导体的模拟成功地预测了临界浓度，并从包含旋转的模拟中呈现了模拟中的本征函数。
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.
