Low-Dimensional Materials Growth, Properties and Performance
We leverage data-driven approaches and advanced computational modeling to uncover processing–structure–property–performance relationships. Current projects include:
- Machine learning–assisted studies of precursors and their interactions with substrates to guide materials synthesis
- Integrated machine learning and thermodynamic modeling for the design of heterostructured materials tailored to electronic applications
- Academic–industry collaboration on semiconductor materials and device development
In collaboration with the Atomic Films Lab