arXiv 1906.022442019-06-05cond-mat.mtrl-sci

半导体的机器学习杂质能级预测：以Cd基硫属化物为例

Machine-learned impurity level prediction for semiconductors: the example of Cd-based chalcogenides

Arun Mannodi-Kanakkithodi, Michael Y. Toriyama, Fatih G. Sen, Michael J. Davis, Robert F. Klie, Maria K. Y. Chan

备注：: 14 pages, 7 figures

摘要：

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以及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数据驱动的机器学习方法变得合适。在这项工作中，我们表明，一个密度泛函理论产生的数据集的杂质在镉基硫族化物CdTe，CdSe和CdS可以导致准确和概括的预测模型的缺陷属性。通过将任何半导体+杂质系统转换成一组数值描述符，建立了杂质形成焓和电荷跃迁能级的回归模型。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，证明虽然仅在终点上训练，但它们适用于中间组合物。我们对5种硫属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，这是由主要的本征缺陷决定的。这些由机器学习预测的主要杂质与DFT预测相比很好，揭示了机器学习模型在快速筛选可能影响半导体光电行为的杂质方面的力量。

英文摘要：

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 suggest a list of impurities which can shift the equilibrium Fermi level in the semiconductor as determined by the dominant intrinsic defects. These dominating impurities as predicted by machine learning 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.

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