Modeling and AI-Guided Understanding of CdTe-Based Photovoltaics

CdTe-based PV devices are currently the only commercially viable alternative to Si. Significant mysteries remain as to the effects of grain boundaries on device performance, and the local environment of dopants and how it relates to activation. In this talk, we will discuss characterization-informed first principles and atomistic modeling of grain boundaries and impurities in CdTe and related systems. The use of x-ray absorption near edge spectra (XANES) and scanning transmission electron microscopy (STEM), in particular, gives unprecedented atomic-level information and the interpretation of XANES and STEM data benefits from AI/ML and modeling.<br/><br/>References:<br/>1. S. Rojsatien, A. K. Mannodi Kanakkithodi, T. Walker, T. Nietzold, E. Colegrove, B. Lai, Z. Cai, M. Holt, M. K. Y. Chan, Mariana I. Bertoni, Quantitative analysis of Cu XANES spectra using linear combination fitting of binary mixtures simulated by FEFF9, Radiation Physics and Chemistry. DOI:10.1016/j.radphyschem.2022.110548.<br/>2. M. Polak, R. Jacobs, A. K. Mannodi Kanakkithodi, M. K. Y. Chan, D. Morgan, Machine Learning for Impurity Charge-State Transition Levels in Semiconductors from Elemental Properties Using Multi-Fidelity Datasets, Journal of Chemical Physics 156, 114110 (2022). DOI:10.1063/5.0083877.<br/>3. A. Mannodi Kanakkithodi, X. Xiang, L. Jacoby, R. Biegaj, S. T. Dunham, D. R. Gamelin, M. K. Y. Chan, Universal Machine Learning Framework for Impurity Level Prediction in Group IV, III-V and II-VI Semiconductors, Patterns 3(3), 100450 (2022). DOI:10.1016/j.patter.2022.100450.<br/>4. F. G. Sen, A. K. Mannodi-Kannakithodi, T. Paulauskas, J. Guo, L. Wang, A. Rockett, M. J. Kim, R. F. Klie, M. K. Y. Chan, Computational Design of Passivants for CdTe Grain Boundaries, Solar Energy and Solar Cells 232, 111279 (2021). DOI:10.1016/j.solmat.2021.111279.<br/>5. A. Mannodi-Kanakkithodi, M. Toriyama, F. G. Sen, M. J. Davis, R. F. Klie, and M. K.Y. Chan, Machine-learned impurity level prediction for semiconductors: the example of Cd-based chalcogenides, npj Computational Materials 6, 39 (2020). DOI:10.1038/s41524-020-0296-7.<br/>6. J. Guo, A. Mannodi-Kanakkithodi, F. G. Sen, E. Schwenker, E. Barnard, A. Munshi, W. Sampath, M. K. Y. Chan, R. F. Klie, Effect of selenium and chlorine co-passivation in polycrystalline CdSeTe devices, Applied Physics Letters 115, 153901 (2019). DOI:10.1063/1.5123169.