Heterostructures of two semiconductors are at the heart of semiconductor devices with tremendous technological importance. The prediction and designing of semiconductor heterostructures of a specific type is a difficult materials science problem, posing a challenge to experimental and computational investigations. In this study, we first establish that the prediction of heterostructure type can be made with good accuracy from the knowledge of the band structure of constituent semiconductors. Following this, we apply machine learning, built on features characterizing constituent semiconductors, on a known dataset of binary semiconductor heterostructures extended by a synthetic minority oversampling technique. A significant feature of engineering made it possible to train a classifier model predicting the heterostructure type with an accuracy of $89\%$. Using the trained model, a large number (872 number) of unknown heterostructure semiconductor types involving elemental and binary semiconductors is theoretically predicted. Interestingly, the developed scheme is found to be extendable to heterojunctions of semiconductor quantum dots.

中文翻译：

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二元半导体异质结构的机器学习分类

两种半导体的异质结构是具有极大技术重要性的半导体器件的核心。特定类型的半导体异质结构的预测和设计是一个困难的材料科学问题，对实验和计算研究提出了挑战。在这项研究中，我们首先建立了从组成半导体的能带结构知识可以很好地预测异质结构类型的方法。然后，我们将基于构成半导体特征的特征的机器学习应用于通过合成少数过采样技术扩展的二进制半导体异质结构的已知数据集。工程的一个重要特点是可以训练一个分类器模型来预测异质结构类型，准确度为。$89％$。使用训练后的模型，从理论上预测了涉及元素半导体和二元半导体的大量未知异质结构半导体类型（872个）。有趣的是，发现所开发的方案可扩展到半导体量子点的异质结。