机器学习预测Bi1-xSbx纳米材料的L点直接带隙
Machine Learning to Predict the L-Point Direct Bandgap of Bi1-xSbx Nanomaterials
摘要：
随着现代纳米科学技术的发展，Bi 1-xSbx可以被合成为不同的纳米尺度和纳米结构，包括薄膜、纳米线、纳米管、纳米带等。然而，由于在布里渊区中的L点处的电子和空穴之间的强关联，当纳米结构化时，直接带在量子限制下以异常的方式演化。由于合金化和低对称性，使用从头计算或kp扰动来预测纳米材料中的L点直接带隙可能在计算上是昂贵的或不准确的。本文尝试用机器学习方法来解决这个问题，包括支持向量回归、回归树、高斯过程回归和人工神经网络。Bi 1-xSbx薄膜和纳米线的拟合优度约为0.99。
英文摘要：
With the development of modern nanoscience and nanotechnology, Bi1-xSbx can be synthesized into different nanoscale and nanostructured forms, including thin films, nanowires, nanotubes, nanoribbons, and many others. However, due to the strong correlation between electrons and holes at the L-point in the Brillouin zone, the direct band evolves in an anomalous manner under the quantum confinement when nanostructured. Due to the alloying and the low symmetry, predicting the L-point direct bandgap in a nanomaterial using either ab initio calculations or kp perturbations can be computationally costive or inaccurate. We here try to solve this problem using the machine learning methods, including the support vector regression, the regression tree, the Gaussian process regression, and the artificial neural network. A goodness-of-fit of ~0.99 can be achieved for Bi1-xSbx thin films and nanowires.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
