Machine learning defect properties in Cd-based chalcogenides

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Impurity energy levels in the band gap can have serious consequences for a semiconductor's performance as a photovoltaic absorber. Data-driven approaches can help accelerate the prediction of point defect properties in common semiconductors, and thus lead to the identification of potential deep lying impurity states. In this work, we use density functional theory (DFT) to compute defect formation energies and charge transition levels of hundreds of impurities in CdX chalcogenide compounds, where X = Te, Se or S. We apply machine learning techniques on the DFT data and develop on-demand predictive models for the formation energy and relevant transition levels of any impurity atom in any site. The trained ML models are general and accurate enough to predict the properties of any possible point defects in any Cd-based chalcogenide, as we prove by testing on a few selected defects in mixed chalcogen compounds CdTe0.5Se0.5 and CdSe0.5S0.5. The ML framework used in this work can be extended to any class of semiconductors.

Original languageEnglish (US)
Title of host publication2019 IEEE 46th Photovoltaic Specialists Conference, PVSC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9781728104942
StatePublished - Jun 2019
Externally publishedYes
Event46th IEEE Photovoltaic Specialists Conference, PVSC 2019 - Chicago, United States
Duration: Jun 16 2019Jun 21 2019

Publication series

NameConference Record of the IEEE Photovoltaic Specialists Conference
ISSN (Print)0160-8371


Conference46th IEEE Photovoltaic Specialists Conference, PVSC 2019
Country/TerritoryUnited States


  • CdTe
  • chalcogenides
  • density functional theory
  • machine learning
  • point defects

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering


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