Machine learning defect properties in Cd-based chalcogenides

Conference · Thu Feb 06 04:00:00 EST 2020

DOI:https://doi.org/10.1109/PVSC40753.2019.8981266· OSTI ID:1872402

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

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 CdTe 0.5 Se 0.5 and CdSe 0.5 S 0.5 . The ML framework used in this work can be extended to any class of semiconductors.

• Research Organization: Argonne National Laboratory (ANL)
• Sponsoring Organization: USDOE Office of Science - Office of Workforce Development for Teachers and Scientists (WDTS); USDOE Office of Science - Office of Basic Energy Sciences - Chemical Sciences, Geosciences, and Biosciences Division; USDOE Office of Energy Efficiency and Renewable Energy (EERE) - Office of Solar Energy Technologies (SETO) - SunShot Initiative
• DOE Contract Number: AC02-06CH11357
• OSTI ID: 1872402
• Country of Publication: United States
• Language: English

Related Subjects

density functional theory
machine learning