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


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.

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Correspondence to Dalia Yablon.

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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).

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