@inproceedings{eb15d449bf794077958375dc0a5cda32,
title = "Machine learning defect properties in Cd-based chalcogenides",
abstract = "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.",
keywords = "CdTe, chalcogenides, density functional theory, machine learning, point defects",
author = "Arun Mannodi-Kanakkithodi and Michael Toriyama and Sen, {Fatih G.} and Davis, {Michael J.} and Klie, {Robert F.} and Chan, {Maria K.Y.}",
note = "Funding Information: We acknowledge funding from the US Department of Energy SunShot program under contract # DOE DEEE005956. Use of the Center for Nanoscale Materials, an Office of Science user facility, was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. This work was supported in part by the U.S. Department of Energy, Office of Science, Office of Workforce Development for Teachers and Scientists (WDTS) under the Science Undergraduate Laboratory Internships Program (SULI). M.J.D. would further like to acknowledge the funding source: U. S. Department of Energy, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences, and Biosciences, under Contract No. DE-AC02-06CH11357. Publisher Copyright: {\textcopyright} 2019 IEEE.; 46th IEEE Photovoltaic Specialists Conference, PVSC 2019 ; Conference date: 16-06-2019 Through 21-06-2019",
year = "2019",
month = jun,
doi = "10.1109/PVSC40753.2019.8981266",
language = "English (US)",
series = "Conference Record of the IEEE Photovoltaic Specialists Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "791--794",
booktitle = "2019 IEEE 46th Photovoltaic Specialists Conference, PVSC 2019",
address = "United States",
}
