Low-Dimensional Materials Growth, Properties and Performance
We harness the power of low-dimensional materials data and advanced computational modeling techniques to reveal processing-structure-property-performance relationships. The projects include:
- Machine learning-assisted study of precursors and their interactions with substrates to guide materials synthesis
- Combined machine learning and thermodynamic calculations to design heterostructured materials for electronic applications
In collaboration with the Atomic Films Lab