Multimodal Quantification of Nanoscale Defect Evolution in Heterostructured Interfaces

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Closely integrated, data-driven synthesis, characterization, and modeling yield new insights into the evolution of defect populations for electronics and quantum computing.
Closely integrated, data-driven synthesis, characterization, and modeling yield new insights into the evolution of defect populations for electronics and quantum computing.

PI: Steven R. Spurgeon

Project Team: Sandra Taylor, Michel Sassi, Bethany Matthews, Sarah Akers, Elizabeth Kautz, Kayla Yano, Yingge Du

Project Collaborators: Ryan Comes (Auburn University)

Project Term: 2 years

Key Science Questions:

  • How do the presence and evolution of atomic-scale defect populations determine the properties and functionality of materials for electronics and quantum computing?
  • Can we develop a data-driven, multi-modal characterization and modeling approach possessing high spatial, chemical, and temporal resolution to quantify and harness these defects?

Project Description: 

We are building a highly integrated experiment and theory workflow based on scanning transmission electron microscopy, atom probe tomography, and ab initio simulations grounded in emerging data science tools. We will examine the evolution of defect populations using different modalities, which will allow us to highlight latent defect signatures and track their evolution in extreme conditions. These insights will unlock a fundamental new understanding of defect formation and kinetics, paving the way for predictive control of emerging materials architectures.