The ability to predict the likelihood of impurity incorporation and their electronic energy levels in semiconductors is crucial for controlling its conductivity, and thus the semiconductor’s performance in solar cells, photodiodes, and optoelectronics. The difficulty and expense of experimental and computational determination of impurity levels makes a data-driven machine learning approach appropriate. In this work, we show that a density functional theory-generated dataset of impurities in Cd-based chalcogenides CdTe, CdSe, and CdS can lead to accurate and generalizable predictive models of defect properties. By converting any semiconductor + impurity system into a set of numerical descriptors, regression models are developed for the impurity formation enthalpy and charge transition levels. These regression models can subsequently predict impurity properties in mixed anion CdX compounds (where X is a combination of Te, Se and S) fairly accurately, proving that although trained only on the end points, they are applicable to intermediate compositions. We make machine-learned predictions of the Fermi-level-dependent formation energies of hundreds of possible impurities in 5 chalcogenide compounds, and we suggest a list of impurities which can shift the equilibrium Fermi level in the semiconductor as determined by the dominant intrinsic defects. Machine learning predictions for the dominating impurities compare well with DFT predictions, revealing the power of machine-learned models in the quick screening of impurities likely to affect the optoelectronic behavior of semiconductors.

预测半导体中杂质掺入的可能性及其电子能级的能力对于控制其电导率至关重要，因此对于控制半导体在太阳能电池，光电二极管和光电子学中的性能至关重要。实验和计算确定杂质水平的难度和费用使得采用数据驱动的机器学习方法较为合适。在这项工作中，我们表明密度泛函理论生成的基于Cd的硫族化物CdTe，CdSe和CdS中的杂质数据集可以导致缺陷性质的准确和可概括的预测模型。通过将任何半导体+杂质系统转换为一组数字描述符，可以为杂质形成焓和电荷跃迁能级建立回归模型。这些回归模型随后可以相当准确地预测混合阴离子CdX化合物（其中X是Te，Se和S的组合）中的杂质性质，证明了尽管仅在终点进行了训练，但它们仍可用于中间组成。我们对5种硫族化物化合物中数百种可能的杂质的费米能级依赖的形成能进行了机器学习的预测，并提出了一系列杂质，这些杂质可以改变半导体中的平衡费米能级，这取决于主要的固有缺陷。机器学习中对主要杂质的预测与DFT预测具有很好的对比，揭示了机器学习模型在快速筛选可能影响半导体光电性能的杂质中的强大功能。