A predictive framework was developed for point-defect formation energies and charge transition levels in zinc blende semiconductors and impurity atoms from across the periodic table. This framework leverages density functional theory (DFT) simulations benchmarked using experimental data, as well as machine learning (ML).
Significance and Impact
Utilizing this framework to identify the better-performing impurities in zinc blende semiconductors will allow scientists to discover new materials for solar and related applications.
Work was performed in part at the Center for Nanoscale Materials
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