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
  • [41] A Comprehensive Survey of Generative Adversarial Networks (GANs) in Cybersecurity Intrusion Detection
    Dunmore, Aeryn
    Jang-Jaccard, Julian
    Sabrina, Fariza
    Kwak, Jin
    IEEE ACCESS, 2023, 11 : 76071 - 76094
  • [42] Urdu Handwritten Ligature Generation Using Generative Adversarial Networks (GANs)
    Sharif, Marium
    Ul-Hasan, Adnan
    Shafait, Faisal
    FRONTIERS IN HANDWRITING RECOGNITION, ICFHR 2022, 2022, 13639 : 421 - 435
  • [43] Generic image application using GANs (Generative Adversarial Networks): A Review
    Porkodi, S. P.
    Sarada, V.
    Maik, Vivek
    Gurushankar, K.
    EVOLVING SYSTEMS, 2023, 14 (05) : 903 - 917
  • [44] What are GANs?: Introducing Generative Adversarial Networks to Middle School Students
    Ali, Safinah
    DiPaola, Daniella
    Breazeal, Cynthia
    THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2021, 35 : 15472 - 15479
  • [45] GIU-GANs: Global Information Utilization for Generative Adversarial Networks
    Tian, Yongqi
    Gong, Xueyuan
    Tang, Jialin
    Su, Binghua
    Liu, Xiaoxiang
    Zhang, Xinyuan
    NEURAL NETWORKS, 2022, 152 : 487 - 498
  • [46] Spectrum of Advancements and Developments in Multidisciplinary Domains for Generative Adversarial Networks (GANs)
    Syed Khurram Jah Rizvi
    Muhammad Ajmal Azad
    Muhammad Moazam Fraz
    Archives of Computational Methods in Engineering, 2021, 28 : 4503 - 4521
  • [47] Hyperparameter Optimization in Generative Adversarial Networks (GANs) Using Gaussian AHP
    Rodrigues, Thiago Serafim
    Pinheiro, Placido Rogerio
    IEEE ACCESS, 2025, 13 : 770 - 788
  • [48] Distributionally robust chance constrained programming with generative adversarial networks (GANs)
    Zhao, Shipu
    You, Fengqi
    AICHE JOURNAL, 2020, 66 (06)
  • [49] Generic image application using GANs (Generative Adversarial Networks): A Review
    S. P. Porkodi
    V. Sarada
    Vivek Maik
    K. Gurushankar
    Evolving Systems, 2023, 14 : 903 - 917
  • [50] Breast Thermographic Image Augmentation Using Generative Adversarial Networks (GANs)
    Vivanco Gualan, Ramiro Israel
    Jimenez Gaona, Yuliana del Cisne
    Castillo Malla, Darwin Patricio
    Rodriguez-Alvarez, Maria Jose
    Lakshminarayanan, Vasudevan
    INFORMATION AND COMMUNICATION TECHNOLOGIES, TICEC 2024, 2025, 2273 : 86 - 99