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

## TL;DR

This paper demonstrates that machine learning models trained on DFT data can accurately predict impurity levels and defect properties in Cd-based chalcogenide semiconductors, aiding rapid impurity screening for optoelectronic applications.

## Contribution

It introduces a data-driven machine learning approach to predict impurity formation energies and charge transition levels in Cd-based chalcogenides, enabling efficient impurity screening.

## Key findings

- Models accurately predict impurity properties in mixed anion compounds.
- Predictions align well with DFT results, validating the approach.
- Machine learning enables quick screening of impurities affecting semiconductor behavior.

## Abstract

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.