Dr. Hafiz Imtiaz

We deveeloped fast machine‑learning method to predict the full electronic band structure of monolayer TMD alloys, which are usually computed using computationally expensive DFT simulations. Using DFT data for alloys made from W, Mo, and S/Se/Te, the researchers trained an “extra trees” model to predict both conduction and valence bands. The model identifies important factors—like the ratio of different atoms—that shape the band structure. It performs very accurately and even works well for new compositions not included in the training set. Unlike models that only predict band gaps, this approach provides complete band information, enabling the calculation of additional useful properties such as effective carrier mass. Overall, the model offers a fast and informative alternative to full DFT simulations for materials research. This work led to this Materials Advances publication.