论文标题
人工神经网络的二维单层SNTE的结构相变
Structural phase transition of two-dimensional monolayer SnTe from artificial neural network
论文作者
论文摘要
随着机器学习在工程和科学中变得越来越重要，不可避免地将机器学习技术应用于材料的研究，尤其是铁电材料中常见的结构相变。在这里，我们构建并培训人工神经网络，以准确预测与原子位移相关的能量变化，并在蒙特卡洛模拟中使用训练有素的人工神经网络在铁电材料上研究其相变。我们将这种方法应用于二维单层SNTE，并表明它确实可以用于模拟相变并预测过渡温度。当可以使用AB ITIBL方法生成的训练数据时，当将人工神经网络视为通用数学结构时，可以轻松地转移到其他铁电材料的研究中。
As machine learning becomes increasingly important in engineering and science, it is inevitable that machine learning techniques will be applied to the investigation of materials, and in particular the structural phase transitions common in ferroelectric materials. Here, we build and train an artificial neural network to accurately predict the energy change associated with atom displacements and use the trained artificial neural network in Monte-Carlo simulations on ferroelectric materials to investigate their phase transitions. We apply this approach to two-dimensional monolayer SnTe and show that it can indeed be used to simulate the phase transitions and predict the transition temperature. The artificial neural network, when viewed as a universal mathematical structure, can be readily transferred to the investigation of other ferroelectric materials when training data generated with ab initio methods are available.
