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Unveiling the Hidden Evolution of Crystal Defects and Disorder in Energy Materials

  • Purdue University

Research output: NLRPresentation

Abstract

Control of point defects and disorder in functional thin films and 2D materials is critical to realizing their full potential in applications ranging from energy storage to advanced electronics. However, these phenomena are often poorly understood, difficult to characterize, and challenging to direct with precision. This presentation explores emerging multi-modal computer vision to decipher and predict order in materials across multiple length scales in the electron microscope, from the atomic to the nanoscale. By fusing data from diverse sources, these powerful models provide unprecedented insights into materials' lifecycles, enabling the control of defects and their associated properties at a fundamental level. This capability promises to transform materials design and accelerate the development of next-generation technologies.
Original languageAmerican English
Number of pages17
DOIs
StatePublished - 2025

Publication series

NamePresented at the 2025 Materials Research Society (MRS) Spring Meeting and Exhibit, 7-11 April 2025, Seattle, Washington

NLR Publication Number

  • NREL/PR-5K00-94250

Keywords

  • adatoms
  • computer vision
  • machine learning
  • mxene
  • point defect
  • vacancies

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