论文标题
机器学习以预测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
论文作者
论文摘要
随着现代纳米科学和纳米技术的发展，可以将BI1-XSBX合成为不同的纳米级和纳米结构形式，包括薄膜，纳米线，纳米管，纳米管，纳米虫等。但是，由于电子和布里渊区L点之间的孔之间的较强相关性，当纳米结构时，直接带在量子约束下以异常的方式演变。由于合金和低对称性，使用Ab tribi算计算或KP扰动可以预测纳米材料中L点直接带隙，这在计算上是昂贵的或不准确的。我们在这里尝试使用机器学习方法来解决此问题，包括支持向量回归，回归树，高斯过程回归和人工神经网络。对于BI1-XSBX薄膜和纳米线，可以实现〜0.99的贴合性。
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.
