SpringerLink
Log in

Image-Based Predictions

  • Chapter
  • First Online:
Machine Learning for Materials Discovery

Part of the book series: Machine Intelligence for Materials Science ((MIMS))

  • 140 Accesses

Abstract

This chapter explores the application of machine learning (ML) algorithms to image-based data for a comprehensive understanding of materials. The focus is on various aspects, including the investigation of structure-property relationships, prediction of ionic conductivity, accelerated property prediction through the combination of finite element analysis and image-based modeling, and the use of molecular dynamics and image-based modeling to predict crack propagation in atomic systems. Additionally, the chapter discusses the use of neural operators to efficiently learn stress and strain fields from limited ground truth data. The integration of ML algorithms with image-based data has shown promising results in advancing materials science, enabling deeper insights into material behavior and accelerating property prediction. Future directions involve the development of more advanced neural operator frameworks, integration with quantum mechanics, exploration of complex material systems, and incorporation of experimental techniques. Overall, the application of ML algorithms to image-based data offers exciting opportunities for materials design and optimization, paving the way for the discovery of novel materials with tailored properties and improved performance in various applications.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now
Chapter
USD 29.95
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (Canada)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (Canada)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. A. Cecen, H. Dai, Y.C. Yabansu, S.R. Kalidindi, L. Song, Material structure-property linkages using three-dimensional convolutional neural networks. Acta Mater. 146, 76–84 (2018)

    Article  ADS  Google Scholar 

  2. R. Kondo, S. Yamakawa, Y. Masuoka, S. Tajima, R. Asahi, Microstructure recognition using convolutional neural networks for prediction of ionic conductivity in ceramics. Acta Mater. 141, 29–38 (2017)

    Article  ADS  Google Scholar 

  3. Y. Jiao, F. Stillinger, S. Torquato, Modeling heterogeneous materials via two-point correlation functions. ii. algorithmic details and applications. Phys. Rev. E 77(3), 031135 (2008)

    Google Scholar 

  4. Y. Jiao, F. Stillinger, S. Torquato, Modeling heterogeneous materials via two-point correlation functions: basic principles. Phys. Rev. E 76(3), 031110 (2007)

    Google Scholar 

  5. Y. Jiao, F. Stillinger, S. Torquato, A superior descriptor of random textures and its predictive capacity. Proc. Natl. Acad. Sci. 106(42), 17634–17639 (2009)

    Google Scholar 

  6. M. Sundararajan, A. Taly, Q. Yan, Axiomatic attribution for deep networks, in International Conference on Machine Learning (PMLR, 2017), pp. 3319–3328

    Google Scholar 

  7. G. Erion, J.D. Janizek, P. Sturmfels, S.M. Lundberg, S.-I. Lee, Improving performance of deep learning models with axiomatic attribution priors and expected gradients. Nat. Mach. Intell. 1–12 (2021)

    Google Scholar 

  8. S.M. Lundberg, S.-I. Lee, A unified approach to interpreting model predictions, in Proceedings of the 31st International Conference on Neural Information Processing Systems (2017), pp. 4768–4777

    Google Scholar 

  9. B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, A. Torralba, Learning deep features for discriminative localization, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 2921–2929

    Google Scholar 

  10. R.R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra, Grad-cam: visual explanations from deep networks via gradient-based localization, in Proceedings of the IEEE International Conference on Computer Vision (2017), pp. 618–626

    Google Scholar 

  11. X. Chen, K. Khor, S. Chan, L. Yu, Influence of microstructure on the ionic conductivity of yttria-stabilized zirconia electrolyte. Mater. Sci. Eng. A 335(1–2), 246–252 (2002)

    Article  Google Scholar 

  12. X. Lei, X. Wu, Z. Zhang, K. **ao, Y. Wang, C. Huang, A machine learning model for predicting the ballistic impact resistance of unidirectional fiber-reinforced composite plate. Sci. Rep. 11(1), (2021). Cited By 0. https://doi.org/10.1038/s41598-021-85963-3

  13. G.X. Gu, C.-T. Chen, M.J. Buehler, De novo composite design based on machine learning algorithm. Extrem. Mech. Lett. 18, 19–28 (2018)

    Article  Google Scholar 

  14. G.X. Gu, C.-T. Chen, D.J. Richmond, M.J. Buehler, Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment. Mater. Horiz. 5(5), 939–945 (2018)

    Article  Google Scholar 

  15. C.-T. Chen, G.X. Gu, Machine learning for composite materials. MRS Commun. 9(2), 556–566 (2019)

    Article  ADS  Google Scholar 

  16. J. Zhang, Y. Li, T. Zhao, Q. Zhang, L. Zuo, K. Zhang, Machine-learning based design of digital materials for elastic wave control. Extrem. Mech. Lett. 48, 101372 (2021). ISSN: 2352-4316. https://doi.org/10.1016/j.eml.2021.101372

  17. O. Keles, Y. He, B. Sirkeci-Mergen, Prediction of elastic stresses in porous materials using fully convolutional networks. Scr. Mater. 197, (2021). https://doi.org/10.1016/j.scriptamat.2021.113805

  18. D. Abueidda, S. Koric, N. Sobh, H. Sehitoglu, Deep learning for plasticity and thermo-viscoplasticity. Int. J. Plast. 136, (2021). Cited By 12. https://doi.org/10.1016/j.ijplas.2020.102852

  19. C. Yang, Y. Kim, S. Ryu, G. X. Gu, Prediction of composite microstructure stress-strain curves using convolutional neural networks. Mater. & Des. 189, 108509 (2020)

    Google Scholar 

  20. A. Yamanaka, R. Kamijyo, K. Koenuma, I. Watanabe, T. Kuwabara, Deep neural network approach to estimate biaxial stress-strain curves of sheet metals. Mater. Des. 195, 108970 (2020). ISSN: 0264-1275. https://doi.org/10.1016/j.matdes.2020.108970

  21. H.T. Kollmann, D.W. Abueidda, S. Koric, E. Guleryuz, N.A. Sobh, Deep learning for topology optimization of 2d metamaterials. Mater. & Des. 196, 109098 (2020)

    Google Scholar 

  22. Z. **, Z. Zhang, K. Demir, G.X. Gu, Machine learning for advanced additive manufacturing. Matter 3(5), 1541–1556 (2020)

    Article  Google Scholar 

  23. X. Li, Z. Liu, S. Cui, C. Luo, C. Li, Z. Zhuang, Predicting the effective mechanical property of heterogeneous materials by image based modeling and deep learning. Comput. Methods Appl. Mech. Eng. 347, 735–753 (2019)

    Article  ADS  MathSciNet  Google Scholar 

  24. Y.-C. Hsu, C.-H. Yu, M.J. Buehler, Using deep learning to predict fracture patterns in crystalline solids. Matter 3(1), 197–211 (2020)

    Article  Google Scholar 

  25. M.M. Rashid, T. Pittie, S. Chakraborty, N.A. Krishnan, Learning the stress-strain fields in digital composites using Fourier neural operator. Iscience 25(11), (2022)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Yardi School of Artificial Intelligence (Joint Appt.), Indian Institute of Technology Delhi, New Delhi, India

    N. M. Anoop Krishnan

  2. Department of Chemical Engineering, Yardi School of Artificial Intelligence (Joint Appt.), Indian Institute of Technology Delhi, New Delhi, India

    Hariprasad Kodamana

  3. Indian Institute of Technology Delhi, New Delhi, India

    Ravinder Bhattoo

Authors
  1. N. M. Anoop Krishnan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hariprasad Kodamana
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ravinder Bhattoo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to N. M. Anoop Krishnan .

Rights and permissions

Reprints and permissions

Copyright information

© 2024 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Krishnan, N.M.A., Kodamana, H., Bhattoo, R. (2024). Image-Based Predictions. In: Machine Learning for Materials Discovery. Machine Intelligence for Materials Science. Springer, Cham. https://doi.org/10.1007/978-3-031-44622-1_14

Download citation

Publish with us

Policies and ethics

Search

Navigation