Machine Learning for Spectroscopy

Presentations

• Teaching Core-Hole Spectroscopy to a Deep Neural Network

  ORAL · Invited

  March 6, 2023, 3:00 PM – March 6, 2023, 3:36 PM

  Presenters

  • Conor Rankine

    • University of York

  Authors

  • Conor Rankine

    • University of York

  • Thomas Penfold

    • Newcastle University

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• AutoML-accelerated EELS/XAS as an advanced structure characterization tool

  ORAL

  March 6, 2023, 3:36 PM – March 6, 2023, 3:48 PM

  Presenters

  • Haili Jia

    • Argonne National Laboratory; Johns Hopkins University

  Authors

  • Haili Jia

    • Argonne National Laboratory; Johns Hopkins University

  • Gihyeok Lee

    • Lawrence Berkeley National Laboratory

  • Yiming Chen

    • Argonne National Laboratory

  • Wanli Yang

    • Lawrence Berkeley National Labrotary
    • Lawrence Berkeley National Laboratory

  • Maria K Chan

    • Argonne National Laboratory

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• Featurization Approaches for Machine Learning of X-ray Absorption Spectra

  ORAL

  March 6, 2023, 3:48 PM – March 6, 2023, 4:00 PM

  Presenters

  • Yiming Chen

    • Argonne National Laboratory

  Authors

  • Yiming Chen

    • Argonne National Laboratory

  • Maria K Chan

    • Argonne National Laboratory

  • Shyue Ping Ong

    • University of California, San Diego

  • Chengjun Sun

    • Argonne National Laboratory
    • Argonne national laboratory

  • Steve M Heald

    • Argonne National Laboratory

  • chi chen

    • University of California, San Diego

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• Multi-code Benchmark on Ti K-edge X-ray Absorption Spectra of Ti-O Compounds

  ORAL

  March 6, 2023, 4:00 PM – March 6, 2023, 4:12 PM

  Presenters

  • Fanchen Meng

    • Brookhaven National Laboratory

  Authors

  • Fanchen Meng

    • Brookhaven National Laboratory

  • Benedikt Maurer

    • Humboldt University of Berlin

  • Fabian Peschel

    • Humboldt University of Berlin

  • Sencer Selcuk

    • Brookhaven National Laboratory

  • Mark S Hybertsen

    • Brookhaven National Laboratory

  • Xiaohui Qu

    • Brookhaven National Laboratory

  • Christian W Vorwerk

    • University of Chicago

  • Claudia Draxl

    • Humboldt University of Berlin

  • John Vinson

    • National Institute of Standards and Tech

  • Deyu Lu

    • Brookhaven National Laboratory

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• Deep Learning and Infrared Spectroscopy: Representation Learning with a β-Variational Autoencoder

  ORAL

  March 6, 2023, 4:12 PM – March 6, 2023, 4:24 PM

  Publication: Grossutti, M., D'Amico, J., Quintal, J., MacFarlane, H., Quirk, A., & Dutcher, J. R. (2022). Deep Learning and Infrared Spectroscopy: Representation Learning with a ß-Variational Autoencoder. The Journal of Physical Chemistry Letters, 13(25), 5787-5793.

  Presenters

  • Michael Grossutti

    • University of Guelph

  Authors

  • Michael Grossutti

    • University of Guelph

  • John R Dutcher

    • Univ of Guelph

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• A machine learning framework for Raman spectrum prediction

  ORAL

  March 6, 2023, 4:24 PM – March 6, 2023, 4:36 PM

  Presenters

  • Maria K Chan

    • Argonne National Laboratory

  Authors

  • Nina Andrejevic

    • Argonne National Laboratory

  • Michael J Davis

    • Argonne National Laboratory

  • Mingda Li

    • Massachusetts Institute of Technology MIT
    • Massachusetts Institute of Technology

  • Maria K Chan

    • Argonne National Laboratory

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• AI-powered biotechnology platform of single-cell Raman micro-spectroscopy enables high-resolution dynamical phenotyping study of bacterial growth and cellular heterogeneity

  ORAL

  March 6, 2023, 4:36 PM – March 6, 2023, 4:48 PM

  Publication: planned papers: Integrated biotechnology platform of single-cell Raman spectroscopy (SCRS) and advanced data analytics enables high-resolution phenotyping study of bacterial growth dynamics and cellular heterogeneity

  Presenters

  • Zijian Wang

    • Cornell University

  Authors

  • Zijian Wang

    • Cornell University

  • Jenny Kao-Kniffin

    • Cornell University

  • Eric J Craft

    • USDA

  • Matthew C Reid

    • Cornell University

  • Andrea Giometto

    • Cornell University
    • Cornell

  • Kilian Q Weinberger

    • Cornell University

  • April Z Gu

    • Cornell University

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• EllipsoNet: Deep-learning-enabled optical ellipsometry for complex thin films

  ORAL

  March 6, 2023, 4:48 PM – March 6, 2023, 5:00 PM

  Presenters

  • Ziyang Wang

    • Rice university

  Authors

  • Ziyang Wang

    • Rice university

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• Exploiting Sparsity in Artificial Neural Networks for Spectroscopic Data

  ORAL

  March 6, 2023, 5:00 PM – March 6, 2023, 5:12 PM

  Presenters

  • Jakub Vrabel

    • CEITEC, Brno University of Technology

  Authors

  • Jakub Vrabel

    • CEITEC, Brno University of Technology

  • Erik Kepes

    • CEITEC, Brno University of Technology

  • Pavel Nedelnik

    • CEITEC, Brno University of Technology

  • Pavel Porizka

    • CEITEC, Brno University of Technology

  • Jozef Kaiser

    • CEITEC, Brno University of Technology

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• Deep machine learning the spectral function of a hole in a quantum antiferromagnet

  ORAL

  March 6, 2023, 5:12 PM – March 6, 2023, 5:24 PM

  Presenters

  • Weiguo Yin

    • Brookhaven National Laboratory

  Authors

  • Weiguo Yin

    • Brookhaven National Laboratory

  • Jackson Lee

    • Rutgers University

  • Matthew R Carbone

    • Brookhaven National Laboratory
    • Computational Science Initiative, Brookhaven National Laboratory

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• A Modernized View of Coherence Pathways in Magnetic Resonance Spectroscopy

  ORAL

  March 6, 2023, 5:24 PM – March 6, 2023, 5:36 PM

  Publication: A Modernized View of Coherence Pathways Applied to Magnetic Resonance Experiments in Unstable, Inhomogeneous Fields
  Alec Angus Beaton, Alexandria Guinness and John Mark Franck
  J. Chem. Phys. (in press) (2022); https://doi.org/10.1063/5.0105388

  Presenters

  • John M Franck

    • Syracuse University

  Authors

  • John M Franck

    • Syracuse University

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• Machine Learning for Improvements to Gamma Spectroscopy in Nuclear Fusion Diagnostics

  ORAL

  March 6, 2023, 5:36 PM – March 6, 2023, 5:48 PM

  Publication: Planned paper: Machine Learning for Improvements to Gamma Spectroscopy in Nuclear Fusion Diagnostics

  Presenters

  • Kimberley S Lennon

    • Sheffield Hallam University

  Authors

  • Kimberley S Lennon

    • Sheffield Hallam University

  • Callum Grove

    • UKAEA

  • Joseph Neilson

    • UKAEA

  • Chantal Nobs

    • UKAEA

  • Lee Packer

    • UKAEA

  • Robin Smith

    • Sheffield Hallam University
    • University of Connecticut

  View abstract →