Skip to main content

Deep learning to establish structure property relationships of impact copolymers from AFM phase images

Abstract

AFM phase images were collected on impact copolymer samples that differ in bulk mechanical properties and microstructure. A deep learning model based on a convolutional neural net (CNN) successfully classified some combinations of ICP’s based on microstructure. A separate regression-based CNN correlated the AFM phase images with various bulk mechanical properties, showing good results with yield strength and ultimate elongation percentage and weak results with flexural modulus and notched izod. The results observed from the deep learning model reveal a relationship between the microstructures as captured by the AFM phase images with the different bulk material properties.

Graphic abstract

This is a preview of subscription content, access via your institution.

Figure 1
Figure 2
Figure 3

References

  1. 1.

    B.L. Mehdi, A. Stevens, L. Kovarik, N. Jiang, H. Mehta, A. Liya, S. Reehl, B. Stanfil, L. Luz, W. Hao, Controlling the spatio-temporal dose distribution during STEM imaging by subsampled acquisition: in-situ observations of kinetic processes in liquids. Appl. Phys. Lett. 115, 063102 (2019)

    Article  Google Scholar 

  2. 2.

    L. Kovarik, A. Stevens, A. Liyu, N.D. Browning, Implementing an accurate and rapid sparse sampling approach for low-dose atomic resolution STEM imaging. Appl. Phys. Lett. 109, 164102 (2016)

    Article  Google Scholar 

  3. 3.

    R. Cohn, E. Holm, Unsupervised machine learning via transfer learning and k-means clustering to classify materials image data. Integr. Mater. Manuf. Innov. (2021). https://doi.org/10.1007/s40192-021-00205-8

    Article  Google Scholar 

  4. 4.

    B. Gallagher, M. Rever, D. Loveland, T.N. Mundhenk, B. Beauchamp, E. Robertson, G.G. Jaman, A.M. Hiszpanski, T. Yong-JinHan, Predicting compressive strength of consolidated molecular solids using computer vision and deep learning. Mater. Des. 190, 108541 (2020)

    Article  Google Scholar 

  5. 5.

    I. Sokolov, M.E. Dokukin, V. Kalaparthi, M. Miljkovic, A. Wang, J.D. Seigne, P. Grivas, E. Demidenko, Noninvasive diagnostic imaging using machine-learning analysis of nanoresolution images of cell surfaces: detection of bladder cancer. PNAS 115, 12920 (2018)

    CAS  Article  Google Scholar 

  6. 6.

    K.P. Kelley, M. Ziatdinov, L. Collins, M.A. Susner, R.K. Vasudevan, N. Balke, S.V. Kalinin, S. Jesse, Fast scanning probe microscopy via machine learning: non-rectangular scans with compressed sensing and Gaussian process optimization. Small (2020). https://doi.org/10.1002/smll.202002878

    Article  Google Scholar 

  7. 7.

    K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition. Computer Vision and Pattern Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)

  8. 8.

    Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521, 436–444 (2015)

    CAS  Article  Google Scholar 

  9. 9.

    M. Abadi et al., Tensorflow: a system for large-scale machine learning, in Symposium on Operating Systems Design and Implementation (2016), pp. 265–283

  10. 10.

    https://github.com/fchollet/keras

  11. 11.

    J.P. Cleveland, B. Anczykowski, A.E. Schmid, V.B. Elings, Energy dissipation in tapping-mode atomic force microscopy. Appl. Phys. Lett. 72, 2613–2615 (1998)

    CAS  Article  Google Scholar 

  12. 12.

    N.F. Martinez, R. Garcia, Measuring phase shifts and energy dissipation with amplitude modulation atomic force microscopy. Nanotechnology 17, S167-172 (2006)

    CAS  Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Dalia Yablon.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Yablon, D., Chakraborty, I., Passino, H. et al. Deep learning to establish structure property relationships of impact copolymers from AFM phase images. MRS Communications (2021). https://doi.org/10.1557/s43579-021-00103-2

Download citation

Keywords

  • Machine learning
  • Scanning probe microscopy (SPM)
  • Polymer