Kim Reyes, Greis J. PhD
Assistant Professor of Physics
kimreyesg@newpaltz.edu
This page highlights undergraduate research completed during Spring 2026 at SUNY New Paltz and presented at the APS Global Physics Summit in Denver, Colorado.
Department of Physics and Astronomy, SUNY New Paltz
This project combines density functional theory and machine learning to predict the band gap of monolayer hexagonal boron nitride (h-BN). Standard PAW-DFT reproduces the optimized lattice constant well but underestimates the band gap. A random-forest model trained on mBJ reference data and physically informed composition descriptors improves the prediction while avoiding the computational cost of higher-level band-gap corrections.
Using 246 monolayer materials, the model achieved a mean absolute error of 0.336 eV and an R2 score of 0.938. For held-out monolayer h-BN, the predicted mBJ band gap was 6.049 eV, close to the 5.917 eV reference value.
This work was supported by the Academic Year Undergraduate Research Experience (AYURE) program at SUNY New Paltz and by the Research, Scholarship, and Creative Activities Office through the Student Opportunity Grant.