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  Machine Learning on Neutron and X-Ray Scattering

Chen, Z., Andrejevic, N., Drucker, N., Nguyen, T., Xian, R. P., Smidt, T., et al. (in preparation). Machine Learning on Neutron and X-Ray Scattering.

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2102.03024.pdf (Preprint), 3MB
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 Creators:
Chen, Zhantao1, 2, Author
Andrejevic, Nina1, 3, Author
Drucker, Nathan1, 4, Author
Nguyen, Thanh1, 5, Author
Xian, R. Patrick6, Author              
Smidt, Tess7, Author
Wang, Yao8, Author
Ernstorfer, Ralph6, Author              
Tennant, Alan9, Author
Chan, Maria10, Author
Li, Mingda1, 5, Author
Affiliations:
1Quantum Matter Group, MIT, Cambridge, MA 02139, USA, ou_persistent22              
2Department of Mechanical Engineering, MIT, Cambridge, MA 02139, USA, ou_persistent22              
3Department of Materials Science and Engineering, MIT, Cambridge, MA 02139, USA, ou_persistent22              
4Department of Applied Physics, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA, ou_persistent22              
5Department of Nuclear Science and Engineering, MIT, Cambridge, MA 02139, USA, ou_persistent22              
6Physical Chemistry, Fritz Haber Institute, Max Planck Society, ou_634546              
7Computational Research Division and Center for Advanced Mathematics for Energy Research Application, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA, ou_persistent22              
8Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA, ou_persistent22              
9Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA, ou_persistent22              
10Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL 60439, USA, ou_persistent22              

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Free keywords: Condensed Matter, Materials Science, cond-mat.mtrl-sci
 Abstract: Neutron and X-ray scattering represent two state-of-the-art materials characterization techniques that measure materials' structural and dynamical properties with high precision. These techniques play critical roles in understanding a wide variety of materials systems, from catalysis to polymers, nanomaterials to macromolecules, and energy materials to quantum materials. In recent years, neutron and X-ray scattering have received a significant boost due to the development and increased application of machine learning to materials problems. This article reviews the recent progress in applying machine learning techniques to augment various neutron and X-ray scattering techniques. We highlight the integration of machine learning methods into the typical workflow of scattering experiments. We focus on scattering problems that faced challenge with traditional methods but addressable using machine learning, such as leveraging the knowledge of simple materials to model more complicated systems, learning with limited data or incomplete labels, identifying meaningful spectra and materials' representations for learning tasks, mitigating spectral noise, and many others. We present an outlook on a few emerging roles machine learning may play in broad types of scattering and spectroscopic problems in the foreseeable future.

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Language(s): eng - English
 Dates: 2021-02-05
 Publication Status: Not specified
 Pages: 56
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: arXiv: 2102.03024
URI: https://arxiv.org/abs/2102.03024
 Degree: -

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