AI Visualization in Nanoscale Microscopy

Artificial Intelligence (AI) and nanotechnology are promising areas for the future of humanity. While deep learning-based computer vision has found applications in many fields from medicine to automotive, its application in nanotechnology can open doors for new scientific discoveries. Can we apply AI to explore objects that our eyes can’t see such as nanoscale-sized objects? An AI platform to visualize nanoscale patterns learnt by a deep learning neural network can open new frontiers for nanotechnology. The objective of this paper is to develop a deep learning-based visualization system on images of nanomaterials obtained by scanning electron microscope (SEM). This paper contributes an AI platform to enable any nanoscience researchers to use AI in the visual exploration of nanoscale morphologies of nanomaterials. This AI is developed by a technique of visualizing intermediate activations of a Convolutional AutoEncoder (CAE). In this method, a nanoscale specimen image is transformed into its feature representations by a Convolution Neural Network (CNN). The convolutional autoencoder is trained on a 100% SEM dataset from NFFA-EUROPE, and then CNN visualization is applied. This AI generates various conceptual feature representations of the nanomaterial. While deep learning-based image classification of SEM images is widely published in literature, there are not many publications that have visualized deep neural networks of nanomaterials. This is significant to gain insights from the learnings extracted by machine learning. This paper unlocks the potential of applying deep learning-based visualization on electron microscopy to offer AI-extracted features and architectural patterns of various nanomaterials. This is a contribution to explainable AI in nanoscale objects, and to learn from otherwise black box neural networks. This paper contributes an open-source AI with reproducible results at URL (https://sites.google.com/view/aifornanotechnology ).