npj：半导体杂质水平的机器学习预测— Cd基硫族化物

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无缺陷、不含杂质的晶体材料是不存在的。实际上，晶体中的缺陷如同原子的规则排列一样重要，很大程度上决定了晶体的性能。众所周知，对于晶体半导体材料，诸如空位、杂质间隙或替代、表面态和晶界之类的缺陷，影响了晶体的光电性能。在没有外部杂质的情况下，固有缺陷决定了半导体内的平衡费米能级，从而决定了导电性（p型、n型或本征）和电荷载流子的性质。杂质原子的引入可以改变电导率，这也取决于主要的天然缺陷，取决于形成焓与费米能级的关系。预测某些杂质对电子结构和材料导电性的影响，对于抑制或有意将它们掺入半导体晶格以实现理想的光电性能至关重要。但预测半导体中杂质产生的电子能级是一件重要的工作，至今却没有简洁的方法。

来自美国阿贡国家实验室纳米材料中心Arun Mannodi-Kanakkithodi和Maria K. Y. Chan共同领导的团队，将杂质原子的元素特性与原子的高能信息和电子信息（均由晶胞缺陷的低成本计算获取）相结合，可得到最佳的特征数据集，以此作为随机森林回归模型的输入数据。作者使用为CdTe、CdSe和CdS生成的具有PBE泛函理论水平的数据，通过对ΔH和（ɛ(q1/q2)）预测模型的训练，能准确地预测混合阴离子化合物CdTe0.5Se0.5 和CdSe0.5S0.5的杂质性质，证明了对样本之外的化合物的预测能力。训练过的模型被用来对5种化合物中杂质的整个化学空间进行预测，然后根据带隙中的费米能级函数获得每种杂质的形成焓。ML预测的结果与DFT的比较显示准确度相近，充分说明这种ML方法可用来成功筛选缺陷位置稳定而有活性的杂质原子。

该文近期发表于npj Computational Materials 6: 39 (2020)，英文标题与摘要如下，点击左下角“阅读原文”可以自由获取论文PDF。

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 

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.

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