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Author Correction:Machine-learned impurity level prediction for semiconductors:the example of Cd-based chalcogenides 被引量:1
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作者 Arun Mannodi-Kanakkithodi Michael Y.Toriyama +3 位作者 fatih g.sen Michael J.Davis Robert F.Klie Maria K.Y.Chan 《npj Computational Materials》 SCIE EI CSCD 2020年第1期558-560,共3页
The authors became aware of a mistake in the original version of this Article.Specifically,some of the band gap values plotted and reported in Fig.1c and Table SI-1 were incorrect.This error originated because two dif... The authors became aware of a mistake in the original version of this Article.Specifically,some of the band gap values plotted and reported in Fig.1c and Table SI-1 were incorrect.This error originated because two different types of k-point meshes were used in DFT computations performed on CdTe,CdSe and CdS:one which is gamma-centered and one which is not gamma-centered. 展开更多
关键词 PREDICTION originated centered
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Machine-learned impurity level prediction for semiconductors:the example of Cd-based chalcogenides
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作者 Arun Mannodi-Kanakkithodi Michael Y.Toriyama +3 位作者 fatih g.sen Michael J.Davis Robert F.Klie Maria K.Y.Chan 《npj Computational Materials》 SCIE EI CSCD 2020年第1期1346-1359,共14页
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... 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. 展开更多
关键词 SEMICONDUCTORS PREDICTION IMPURITY
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