BetaCitation Atlas

RESEARCH PAPER · IEEE International Conference on Big Data (BigData) · 2016

Materials discovery: Understanding polycrystals from large-scale electron patterns

Ruoqian Liu, Ankit Agrawal, Wei‐keng Liao, Alok Choudhary, Marc De Graef

Read original paper ↗

See where this research leads.

30 direct citing papers in this saved network. Explore the next layer with second-order citations.

Open in your workspace →Saved 2026-10-14 · complete coverage
Filter Citing Papers
Press Enter or comma to add · × to remove · searches titles and available abstracts
Journals
All journals
Most represented first · select one or more
Explore Terms In These PapersHide SuggestionsShow Suggestions

Original and current center papers stay visible as anchors.
Numbers show citations received
42 direct citing works

Materials discovery: Understanding polycrystals from large-scale electron patternsOriginalContext Aware Machine Learning Approaches for Modeling Elastic Localization in Three-Dimensional Composite Microstructures48An online tool for predicting fatigue strength of steel alloys based on ensemble data mining101Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets365Extracting Grain Orientations from EBSD Patterns of Polycrystalline Materials Using Convolutional Neural Networks66Extracting dislocation microstructures by deep learning48Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches192Microstructure optimization with constrained design objectives using machine learning-based feedback-aware data-generation47Convolutional neural network-based method for real-time orientation indexing of measured electron backscatter diffraction patterns55Deep learning methods based on cross-section images for predicting effective thermal conductivity of composites3Deep materials informatics: Applications of deep learning in materials science294Recent advances and applications of machine learning in solid-state materials science2607Information Fusion for Multi-Source Material Data: Progress and Challenges50Materials Image Informatics Using Deep Learning11Deep learning analysis on microscopic imaging in materials science195Quantitative microstructure analysis for solid-state metal additive manufacturingviadeep learning42Indexing of electron back-scatter diffraction patterns using a convolutional neural network52Acceleration of Neural Network Training on Hardware via HLS for an Edge-AI Device2Convolutional neural network-assisted recognition of nanoscale L12 ordered structures in face-centred cubic alloys37Machine learning in materials science: From explainable predictions to autonomous design281Machine Learning Based Methods for Obtaining Correlations between Microstructures and Thermal Stresses18Design of Phononic Bandgap Metamaterials Based on Gaussian Mixture Beta Variational Autoencoder and Iterative Model Updating43Recent advances and applications of deep learning methods in materials science1161Deep Learning CNN for the Prediction of Grain Orientations on EBSD Patterns of AA5083 Alloy5Contribution of advanced orientation microscopy techniques for the analysis of phase transformation microstructures in steels0Tailoring the mechanical properties of 3D microstructures: A deep learning and genetic algorithm inverse optimization framework53Deep learning and correlative microscopy for quantification of grain orientation in sintered FeNdB-type permanent magnets by domain pattern analysis11Manufacturability-aware deep generative design of 3D metamaterial units for additive manufacturing26Designing Connectivity-Guaranteed Porous Metamaterial Units Using Generative Graph Neural Networks3New Intelligent Computing Techniques for Engineering Materials—A Comprehensive Review0An uncertainty-aware deep learning framework-based robust design optimization of metamaterial units14

Arrows point to the paper being cited. Selecting a paper shows its incoming citations and its known path back to the original paper.

Drag to pan · Use + / − to zoom · Escape to close43 nodes / 53 citation links
Browse Matching Papers

Papers that cite this work

30 direct citing papers saved.

  1. Context Aware Machine Learning Approaches for Modeling Elastic Localization in Three-Dimensional Composite Microstructures

    Integrating materials and manufacturing innovation · 2017-03-31

    Ruoqian Liu, Yuksel C. Yabansu, Zijiang Yang, Alok Choudhary, Surya R. Kalidindi, Ankit Agrawal

    Read original paper ↗ ·

  2. An online tool for predicting fatigue strength of steel alloys based on ensemble data mining

    International Journal of Fatigue · 2018-04-26

    Ankit Agrawal, Alok Choudhary

    Read original paper ↗ ·

  3. Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets

    Computational Materials Science · 2018-05-25

    Zijiang Yang, Yuksel C. Yabansu, Reda Al-Bahrani, Wei‐keng Liao, Alok Choudhary, Surya R. Kalidindi, Ankit Agrawal

    Read original paper ↗ ·

  4. Extracting Grain Orientations from EBSD Patterns of Polycrystalline Materials Using Convolutional Neural Networks

    Microscopy and Microanalysis · 2018-10-01

    Dipendra Jha, Saransh Singh, Reda Al-Bahrani, Wei‐keng Liao, Alok Choudhary, Marc De Graef, Ankit Agrawal

    Read original paper ↗ ·

  5. Extracting dislocation microstructures by deep learning

    International Journal of Plasticity · 2018-11-16

    Yuqi Zhang, Alfonso H.W. Ngan

    Read original paper ↗ ·

  6. Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches

    Acta Materialia · 2018-12-31

    Zijiang Yang, Yuksel C. Yabansu, Dipendra Jha, Wei‐keng Liao, Alok Choudhary, Surya R. Kalidindi, Ankit Agrawal

    Read original paper ↗ ·

  7. Microstructure optimization with constrained design objectives using machine learning-based feedback-aware data-generation

    Computational Materials Science · 2019-01-27

    Arindam Paul, Pınar Acar, Wei‐keng Liao, Alok Choudhary, Veera Sundararaghavan, Ankit Agrawal

    Read original paper ↗ ·

  8. Convolutional neural network-based method for real-time orientation indexing of measured electron backscatter diffraction patterns

    Acta Materialia · 2019-03-27

    Yufeng Shen, Reeju Pokharel, Thomas J. Nizolek, Anil Kumar, Turab Lookman

    Read original paper ↗ ·

  9. Deep learning methods based on cross-section images for predicting effective thermal conductivity of composites

    arXiv (Cornell University) · 2019-04-12

    Qingyuan Rong, Wei Han, Hua Bao

    Read original paper ↗ ·

  10. Deep materials informatics: Applications of deep learning in materials science

    MRS Communications · 2019-06-13

    Ankit Agrawal, Alok Choudhary

    Read original paper ↗ ·

  11. Recent advances and applications of machine learning in solid-state materials science

    npj Computational Materials · 2019-08-08

    Jonathan Schmidt, Mário R. G. Marques, Silvana Botti, Miguel A. L. Marques

    Read original paper ↗ ·

  12. Information Fusion for Multi-Source Material Data: Progress and Challenges

    Applied Sciences · 2019-08-22

    Jingren Zhou, Xin Hong, Peiquan Jin

    Read original paper ↗ ·

  13. Materials Image Informatics Using Deep Learning

    WORLD SCIENTIFIC eBooks · 2020-03-19

    Ankit Agrawal, Kasthurirangan Gopalakrishnan, Alok Choudhary

    Read original paper ↗ ·

  14. Deep learning analysis on microscopic imaging in materials science

    Materials Today Nano · 2020-06-04

    M. Ge, Fei Su, Zhicheng Zhao, D. Su

    Read original paper ↗ ·

  15. Quantitative microstructure analysis for solid-state metal additive manufacturingviadeep learning

    Journal of materials research/Pratt's guide to venture capital sources · 2020-06-24

    Yi Han, R. Joey Griffiths, Hang Z. Yu, Yunhui Zhu

    Read original paper ↗ ·

  16. Indexing of electron back-scatter diffraction patterns using a convolutional neural network

    Acta Materialia · 2020-08-24

    Zili Ding, Elena Pascal, Marc De Graef

    Read original paper ↗ ·

  17. Acceleration of Neural Network Training on Hardware via HLS for an Edge-AI Device

    Innovations in Intelligent Systems and Applications Conference (ASYU) · 2020-10-15

    Alican Özeloğlu, İsmail San

    Read original paper ↗ ·

  18. Convolutional neural network-assisted recognition of nanoscale L12 ordered structures in face-centred cubic alloys

    npj Computational Materials · 2021-01-05

    Yue Li, Xuyang Zhou, Timoteo Colnaghi, Ye Wei, Andreas Marek A, Hongxiang Li, Stefan Bauer, Markus Rampp, Leigh T. Stephenson

    Read original paper ↗ ·

  19. Machine learning in materials science: From explainable predictions to autonomous design

    Computational Materials Science · 2021-03-10

    Ghanshyam Pilania

    Read original paper ↗ ·

  20. Machine Learning Based Methods for Obtaining Correlations between Microstructures and Thermal Stresses

    Metals · 2021-07-22

    Akshay Bhutada, Sunni Kumar, Dayalan Gunasegaram, Alankar Alankar

    Read original paper ↗ ·

  21. Design of Phononic Bandgap Metamaterials Based on Gaussian Mixture Beta Variational Autoencoder and Iterative Model Updating

    Journal of Mechanical Design · 2022-02-10

    Zihan Wang, Weikang Xian, Mohamed Ridha Baccouche, Horst Lanzerath, Ying Li, Hongyi Xu

    Read original paper ↗ ·

  22. Recent advances and applications of deep learning methods in materials science

    npj Computational Materials · 2022-04-05

    Kamal Kumar Choudhary, Brian DeCost, Chi Chen, Anubhav Jain, Francesca Tavazza, Ryan Cohn, Cheol Woo Park, Alok Choudhary, Ankit Agrawal, Simon J. L. Billinge, Elizabeth A. Holm, Shyue Ping Ong, Chris M. Wolverton

    Read original paper ↗ ·

  23. Deep Learning CNN for the Prediction of Grain Orientations on EBSD Patterns of AA5083 Alloy

    Engineering Technology & Applied Science Research · 2022-04-09

    Dhia Kadhm Suker

    Read original paper ↗ ·

  24. Contribution of advanced orientation microscopy techniques for the analysis of phase transformation microstructures in steels

    Unknown journal · 2022-12-13

    T Martinez Ostormujof

    Read original paper ↗ ·

  25. Tailoring the mechanical properties of 3D microstructures: A deep learning and genetic algorithm inverse optimization framework

    Materials Today · 2023-10-16

    Xiao Shang, Zhiying Liu, Jiahui Zhang, Tianyi Lyu, Yu Zou

    Read original paper ↗ ·

  26. Deep learning and correlative microscopy for quantification of grain orientation in sintered FeNdB-type permanent magnets by domain pattern analysis

    Acta Materialia · 2023-11-28

    Amit Kumar Choudhary, Tvrtko Grubesa, Andreas Jansche, Timo Bernthaler, D. Goll, Gerhard Schneider

    Read original paper ↗ ·

  27. Manufacturability-aware deep generative design of 3D metamaterial units for additive manufacturing

    Structural and Multidisciplinary Optimization · 2024-02-01

    Zihan Wang, Hongyi Xu

    Read original paper ↗ ·

  28. Designing Connectivity-Guaranteed Porous Metamaterial Units Using Generative Graph Neural Networks

    Journal of Mechanical Design · 2024-08-02

    Zihan Wang, Austin M. Bray, Kiarash Naghavi Khanghah, Hongyi Xu

    Read original paper ↗ ·

  29. New Intelligent Computing Techniques for Engineering Materials—A Comprehensive Review

    Lecture notes in networks and systems · 2025-01-01

    Sonal Talreja

    Read original paper ↗ ·

  30. An uncertainty-aware deep learning framework-based robust design optimization of metamaterial units

    Structural and Multidisciplinary Optimization · 2025-03-01

    Zihan Wang, Anindya Bhaduri, Hongyi Xu, Liping Wang

    Read original paper ↗ ·
Citation Atlas · A map of research, not a ranking.