Novel applications of generative adversarial networks (GANs) in the analysis of ultrafast electron diffraction (UED) images

被引:0
|
作者
Daoud, Hazem [1 ]
Sirohi, Dhruv [2 ]
Mjeku, Endri [3 ]
Feng, John [1 ]
Oghbaey, Saeed [1 ]
Miller, R. J. Dwayne [4 ]
机构
[1] Univ Toronto, Dept Phys, Toronto, ON M5S 1A7, Canada
[2] Univ Toronto, Dept Engn Sci, Toronto, ON M5S 1A7, Canada
[3] Univ Toronto, Dept Math, Toronto, ON M5S 1A7, Canada
[4] Univ Toronto, Dept Phys & Chem, Toronto, ON M5S 3H6, Canada
来源
JOURNAL OF CHEMICAL PHYSICS | 2023年 / 159卷 / 04期
关键词
MAPPING ATOMIC MOTIONS; ULTRABRIGHT ELECTRONS; CAPTURING CHEMISTRY;
D O I
10.1063/5.0154871
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Inferring transient molecular structural dynamics from diffraction data is an ambiguous task that often requires different approximation methods. In this paper, we present an attempt to tackle this problem using machine learning. Although most recent applications of machine learning for the analysis of diffraction images apply only a single neural network to an experimental dataset and train it on the task of prediction, our approach utilizes an additional generator network trained on both synthetic and experimental data. Our network converts experimental data into idealized diffraction patterns from which information is extracted via a convolutional neural network trained on synthetic data only. We validate this approach on ultrafast electron diffraction data of bismuth samples undergoing thermalization upon excitation via 800 nm laser pulses. The network was able to predict transient temperatures with a deviation of less than 6% from analytically estimated values. Notably, this performance was achieved on a dataset of 408 images only. We believe that employing this network in experimental settings where high volumes of visual data are collected, such as beam lines, could provide insights into the structural dynamics of different samples.
引用
收藏
页数:10
相关论文
共 50 条
  • [1] Applications of Generative Adversarial Networks (GANs): An Updated Review
    Alqahtani, Hamed
    Kavakli-Thorne, Manolya
    Kumar, Gulshan
    ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING, 2021, 28 (02) : 525 - 552
  • [2] Applications of Generative Adversarial Networks (GANs): An Updated Review
    Hamed Alqahtani
    Manolya Kavakli-Thorne
    Gulshan Kumar
    Archives of Computational Methods in Engineering, 2021, 28 : 525 - 552
  • [3] On the photographic status of images produced by generative adversarial networks (GANs)
    Somaini, Antonio
    PHILOSOPHY OF PHOTOGRAPHY, 2022, 13 (01) : 153 - 164
  • [4] Creating Artificial Images for Radiology Applications Using Generative Adversarial Networks (GANs) - A Systematic Review
    Sorin, Vera
    Barash, Yiftach
    Konen, Eli
    Klang, Eyal
    ACADEMIC RADIOLOGY, 2020, 27 (08) : 1175 - 1185
  • [5] Ultrasound breast images denoising using generative adversarial networks (GANs)
    Jimenez-Gaona, Yuliana
    Rodriguez-Alvarez, Maria Jose
    Escudero, Lider
    Sandoval, Carlos
    Lakshminarayanan, Vasudevan
    INTELLIGENT DATA ANALYSIS, 2024, 28 (06) : 1661 - 1678
  • [6] Generative Adversarial Networks (GANs) in Medical Imaging: Advancements, Applications, and Challenges
    Islam, Showrov
    Aziz, Md. Tarek
    Nabil, Hadiur Rahman
    Jim, Jamin Rahman
    Mridha, M. F.
    Kabir, Md. Mohsin
    Asai, Nobuyoshi
    Shin, Jungpil
    IEEE ACCESS, 2024, 12 : 35728 - 35753
  • [7] Generative adversarial networks (GANs): Introduction, Taxonomy, Variants, Limitations, and Applications
    Sharma P.
    Kumar M.
    Sharma H.K.
    Biju S.M.
    Multimedia Tools and Applications, 2024, 83 (41) : 88811 - 88858
  • [8] Recent Progress on Generative Adversarial Networks (GANs): A Survey
    Pan, Zhaoqing
    Yu, Weijie
    Yi, Xiaokai
    Khan, Asifullah
    Yuan, Feng
    Zheng, Yuhui
    IEEE ACCESS, 2019, 7 : 36322 - 36333
  • [9] Applications of Generative Adversarial Networks (GANs) in Positron Emission Tomography (PET) imaging: A review
    Ioannis D. Apostolopoulos
    Nikolaos D. Papathanasiou
    Dimitris J. Apostolopoulos
    George S. Panayiotakis
    European Journal of Nuclear Medicine and Molecular Imaging, 2022, 49 : 3717 - 3739
  • [10] Applications of Generative Adversarial Networks (GANs) in Positron Emission Tomography (PET) imaging: A review
    Apostolopoulos, Ioannis D.
    Papathanasiou, Nikolaos D.
    Apostolopoulos, Dimitris J.
    Panayiotakis, George S.
    EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2022, 49 (11) : 3717 - 3739