Theory+AI/ML for microscopy and spectroscopy: Challenges and opportunities

Davis Unruh, Venkata Surya Chaitanya Kolluru, Arun Baskaran, Yiming Chen, Maria K.Y. Chan*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

13 Scopus citations

Abstract

Abstract: Advances in instrumentation for experimental characterization of materials such as microscopy and spectroscopy have led to an explosion in information available on materials chemistry, structures, and transformations. But the interpretation of microscopy and spectroscopy data is increasingly challenging due to the increasing volume and complexity of these data. In this article, we discuss the use of theoretical modeling, artificial intelligence/machine learning (AI/ML), and AI/ML in conjunction with theory, for the interpretation of microscopy and spectroscopy data. Graphical abstract: [Figure not available: see fulltext.].

Original languageEnglish (US)
Pages (from-to)1024-1035
Number of pages12
JournalMRS Bulletin
Volume47
Issue number10
DOIs
StatePublished - Oct 2022

Keywords

  • Artificial intelligence
  • Electron microscopy
  • Machine learning
  • Scanning tunneling microscopy
  • X-ray

ASJC Scopus subject areas

  • General Materials Science
  • Condensed Matter Physics
  • Physical and Theoretical Chemistry

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