Machine learning for analyzing atomic force microscopy (AFM) images generated from polymer blends†

Abstract

In this paper, we present a new machine learning (ML) workflow with unsupervised learning techniques to identify domains within atomic force microscopy (AFM) images obtained from polymer films. The goal of the workflow is to (i) identify the spatial location of two types of polymer domains with little to no manual intervention (Task 1) and (ii) calculate the domain size distributions, which in turn can help qualify the phase separated state of the material as macrophase or microphase ordered/disordered domains (Task 2). We briefly review existing approaches used in other fields – computer vision and signal processing – that can be applicable to the above tasks frequently encountered in the field of polymer science and engineering. We then test these approaches from computer vision and signal processing on the AFM image dataset to identify the strengths and limitations of each of these approaches for our first task. For our first domain segmentation task, we found that the workflow using discrete Fourier transform (DFT) or discrete cosine transform (DCT) with variance statistics as the feature works the best. The popular ResNet50 deep learning approach from the computer vision field exhibited relatively poorer performance in the domain segmentation task for our AFM images as compared to the DFT and DCT based workflows. For the second task, for each of the 144 input AFM images, we then used an existing Porespy Python package to calculate the domain size distribution from the output of that image from the DFT-based workflow. The information and open-source codes we share in this paper can serve as a guide for researchers in the fields of polymers and soft materials who need ML modeling and workflows for automated analyses of AFM images from polymer samples that may have crystalline/amorphous domains, sharp/rough interfaces between domains, or micro- or macro-phase separated domains.