| 3 | Machine-learned impurity level prediction for semiconductors:the example of Cd-based chalcogenides显示文摘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. | Arun Mannodi-Kanakkithodi Michael Y.Toriyama Fatih G.Sen Michael J.Davis Robert F.Klie Maria K.Y.Chan | 2020 | npj Computational Materials2020，，1： | 0 |
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