Deep Learning Spectroscopy

Presentations

• Prediction of materials properties from core-loss spectrum using neural network

  ORAL · Invited

  March 17, 2022, 8:00 AM – March 17, 2022, 8:36 AM

  Presenters

  • Teruyasu Mizoguchi

    • The University of Tokyo
    • University of Tokyo

  Authors

  • Teruyasu Mizoguchi

    • The University of Tokyo
    • University of Tokyo

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• Review and Prospect: Deep Learning in Nuclear Magnetic Resonance Spectroscopy

  ORAL · Invited

  March 17, 2022, 8:36 AM – March 17, 2022, 9:12 AM

  Presenters

  • Xiaobo Qu

    • Department of Electronic Science, Xiamen University

  Authors

  • Xiaobo Qu

    • Department of Electronic Science, Xiamen University

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• Investigation of featurization approaches for supervised machine learning in X-ray spectroscopy

  ORAL

  March 17, 2022, 9:12 AM – March 17, 2022, 9:24 AM

  Presenters

  • Yiming Chen

    • University of California, San Diego

  Authors

  • Yiming Chen

    • University of California, San Diego

  • Chi Chen

    • University of California, San Diego

  • Chengjun Sun

    • Argonne National Laboratory

  • Steve M Heald

    • Argonne National Laboratory

  • Shyue Ping Ong

    • University of California, San Diego

  • Maria K Chan

    • Argonne National Laboratory

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• Combining machine learning and XANES spectra featurization to make chemical environment predictions of CdTe materials

  ORAL

  March 17, 2022, 9:24 AM – March 17, 2022, 9:36 AM

  Presenters

  • Justin Pothoof

    • University of Washington

  Authors

  • Justin Pothoof

    • University of Washington

  • Arun Kumar Mannodi Kanakkithodi

    • Purdue University

  • Srisuda Rojsatien

    • Arizona State University

  • Xinyue Wang

    • University of Washington

  • Amy Stegmann

    • University of Washington

  • Yu-Hsuan Hsiao

    • University of Washington

  • Mariana Bertoni

    • Arizona State University

  • Maria K Chan

    • Argonne National Laboratory

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• Deep-learning-enabled optical ellipsometry for complex thin films and 2D materials

  ORAL

  March 17, 2022, 9:36 AM – March 17, 2022, 9:48 AM

  Presenters

  • ziyang wang

    • The Pennsylvania State University

  Authors

  • ziyang wang

    • The Pennsylvania State University

  • Yuxuan Lin

    • University of California, Berkeley

  • Shengxi Huang

    • The Pennsylvania State University
    • Pennsylvania State University
    • Department of Electrical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States

  • Kunyan Zhang

    • Pennsylvania State University

  • Wenjing Wu

    • Columbia University

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• Machine Learning-Accelerated Spectral Imaging Analysis for Nanomaterials

  ORAL

  March 17, 2022, 9:48 AM – March 17, 2022, 10:00 AM

  Publication: H. Jia, C. Wang, C. Wang, P. Clancy. Machine Learning-Accelerated Spectral Imaging Analysis for Nanomaterials. Nano Letters. 2022. (in prep.)

  Presenters

  • Haili Jia

    • Johns Hopkins University

  Authors

  • Haili Jia

    • Johns Hopkins University

  • Canhui Wang

    • Johns Hopkins University

  • Chao Wang

    • Johns Hopkins University

  • Paulette Clancy

    • Johns Hopkins University

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• Elucidating proximity magnetism through polarized neutron reflectometry and machine learning

  ORAL

  March 17, 2022, 10:00 AM – March 17, 2022, 10:12 AM

  Publication: N. Andrejevic, Z. Chen, et al. "Elucidating proximity magnetism through polarized neutron reflectometry and machine learning." arXiv preprint arXiv:2109.08005 (2021).

  Presenters

  • Nina Andrejevic

    • Massachusetts Institute of Technology MI

  Authors

  • Nina Andrejevic

    • Massachusetts Institute of Technology MI

  • Zhantao Chen

    • Massachusetts Institute of Technology MI
    • Massachusetts Institute of Technology

  • Thanh Nguyen

    • Massachusetts Institute of Technology MI

  • Mingda Li

    • Massachusetts Institute of Technology
    • Massachusetts Institute of Technology MI

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• Predicting X-Ray Absorption Spectra of Materials Using Graph-based Neural Networks

  ORAL

  March 17, 2022, 10:12 AM – March 17, 2022, 10:24 AM

  Presenters

  • Fanchen Meng

    • Brookhaven National Laboratory

  Authors

  • Fanchen Meng

    • Brookhaven National Laboratory

  • Matthew R Carbone

    • Brookhaven National Laboratory

  • Deyu Lu

    • Brookhaven National Laboratory

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• Identifying charge density and dielectric environment of graphene using Raman spectroscopy and deep learning

  ORAL

  March 17, 2022, 10:24 AM – March 17, 2022, 10:36 AM

  Presenters

  • Zhuofa Chen

    • Boston University

  Authors

  • Zhuofa Chen

    • Boston University

  • Yousif Khaireddin

    • Boston University

  • Anna K Swan

    • Boston University

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