Machine Learning Advances Semiconductor Modeling

June 2, 2022

For a study published in Patterns, researchers developed a predictive framework for zinc blende semiconductors and impurity atoms from across the periodic table.

Comparison of DFT-computed defect levels with experimentally measured levels. Measured versus DFT RMSE values are also shown for different semiconductor types and for the combined set of points. A few defect levels have been labeled.

Scientific Achievement

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.

Research Details

•Developed large computational dataset of defect properties in zinc blende semiconductors.

•Used ML-based regression algorithms to train predictive models for defect properties.

•Generated lists of low-energy “dominating” impurities, which tune equilibrium conductivity of semiconductors.

DOI: https://doi.org/10.1016/j.patter.2022.100450

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Work was performed in part at the Center for Nanoscale Materials

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