Challenges and Opportunities of Machine Learning on Neutron and X-ray Scattering
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Challenges and Opportunities of Machine Learning on Neutron and X-ray Scattering.pdf
Description
Published version
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637.28 KB
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Checksum (MD5)
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Author(s)
Drucker, Nathan C
Liu, Tongtong
Chen, Zhantao
Okabe, Ryotaro
Chotrattanapituk, Abhijatmedhi
Nguyen, Thanh
Wang, Yao
Li, Mingda
Date Issued
October 12, 2022
Journal
Synchrotron Radiation News
Publisher
Taylor & Francis
Citation
Drucker, N. C., Liu, T., Chen, Z., Okabe, R., Chotrattanapituk, A., Nguyen, T., … Li, M. (2022). Challenges and Opportunities of Machine Learning on Neutron and X-ray Scattering. Synchrotron Radiation News, 35(4), 16–20.
Version
Final published version
Abstract
Machine learning has been highly successful in boosting the re-search for neutron and X-ray scattering in the past few years [1, 2]. Fordiffraction, machine learning has shown great promise in phase map-ping [3, 4] and crystallographic information determination [5, 6]. Insmall-angle scattering, machine learning shows the power in reachingsuper-resolution [7, 8], reconstructing structures for macromolecules[9], and building structure-property relations [10]. As for absorptionspectroscopy, machine learning has enabled the rapid inverse searchfor optimized structures [11, 12] with improved spectral interpretability[13, 14]. Overall, as a data-driven approach, the success of the machine-learning-based scattering analysis depends on a few criteria, including:• Quantity of available experimental data, and feasibility to extractcertain data labels;• Quality of experimental data that can separate the intrinsic effect(e.g., materials properties) from extrinsic influence (e.g., instru-mental or data artifacts);• Feasibility to generate high volume of computational data;• Accuracy of computational data that can simulate the experimen-tal data.
MIT Department
Massachusetts Institute of Technology. Department of Physics
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Department of Chemistry
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Nuclear Science and Engineering
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DOI of Published Version
https://doi.org/10.1080/08940886.2022.2112498
