Skip to main content

Low-Dimensional Materials Growth

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
mx2-Hetero
Graph

 

 

 

 

In collaboration with the Atomic Films Lab