  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.
