Assistant Professor, Chemical and Biological Engineering
Our group uses quantum mechanics, high-throughput computing, and machine learning to discover and design novel materials for next-generation microelectronics. We are deeply interested in the predictive modeling of defects/doping of semiconductors.
1. C.-W. Lee, K. Yazawa, A. Zakutayev, G. L. Brennecka, and P. Gorai, Switching it Up: New Mechanisms Revealed in Wurtzite-type Ferroelectrics, Science Advances 10, eadl0848 (2024).
2. C.-W. Lee, N. Ud Din, K. Yazawa, G. L. Brennecka, A. Zakutayev, and P. Gorai, Emerging Materials and Design Principles for Wurtzite-Type Ferroelectrics, Matter 7, P1644 (2024).
3. M. Y. Toriyama, A. Carranco, G. J. Snyder, and P. Gorai, Material Descriptors to Predict Thermoelectric Performance of Narrow-gap Semiconductors and Semimetals, Materials Horizons 10, 4256 (2023).
4. J. Qu, A. Balvanz, S. Baranets, S. Bobev, and P. Gorai, Computational Design of Thermoelectric Alloys Through Optimization of Transport and Dopability, Materials Horizons 9, 720 (2022).
5. P. Gorai, R. W. McKinney, N. M. Haegel, A. Zakutayev, and V. Stevanovic, A Computational Survey of Semiconductors for Power Electronics, Energy & Environmental Science, 12, 3338 (2019).