@article{1132d9deb17244ed8cc9adce52a0632f,
title = "Theory+AI/ML for microscopy and spectroscopy: Challenges and opportunities",
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.].",
keywords = "Artificial intelligence, Electron microscopy, Machine learning, Scanning tunneling microscopy, X-ray",
author = "Davis Unruh and Kolluru, {Venkata Surya Chaitanya} and Arun Baskaran and Yiming Chen and Chan, {Maria K.Y.}",
note = "Funding Information: M.C. and D.U. acknowledge the support from the BES SUFD Early Career Award. Work performed at the Center for Nanoscale Materials, a US Department of Energy (DOE) Office of Science User Facility, was supported by the US DOE, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357. This work is supported, in part, by the US DOE Office of Science Scientific User Facilities project titled “Integrated Platform for Multimodal Data Capture, Exploration and Discovery Driven by AI Tools.” This work is supported, in part, by the US DOE Office of Science Scientific User Facilities AI/ML project titled, “A Digital Twin for Spatiotemporally Resolved Experiments.” This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the US DOE under Contract No. DE-AC02-05CH11231. Publisher Copyright: {\textcopyright} 2023, UChicago Argonne, LLC, Operator of Argonne National Laboratory, under exclusive License to the Materials Research Society.",
year = "2022",
month = oct,
doi = "10.1557/s43577-022-00446-8",
language = "English (US)",
volume = "47",
pages = "1024--1035",
journal = "MRS Bulletin",
issn = "0883-7694",
publisher = "Materials Research Society",
number = "10",
}