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With the development of modern nanoscience and nanotechnology, Bi1-xSbx canbe synthesized into different nanoscale and nanostructured forms, includingthin films, nanowires, nanotubes, nanoribbons, and many others. However, due tothe strong correlation between electrons and holes at the L-point in theBrillouin zone, the direct band evolves in an anomalous manner under thequantum confinement when nanostructured. Due to the alloying and the lowsymmetry, predicting the L-point direct bandgap in a nanomaterial using eitherab initio calculations or kp perturbations can be computationally costive orinaccurate. We here try to solve this problem using the machine learningmethods, including the support vector regression, the regression tree, theGaussian process regression, and the artificial neural network. Agoodness-of-fit of ~0.99 can be achieved for Bi1-xSbx thin films and nanowires.

Machine Learning to Predict the L-Point Direct Bandgap of Bi1-xSbx Nanomaterials