  Artificial Intelligence & 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 nano scale 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. This paper contributes
an AI platform to enable any nanoscience researcher to use AI in visual
exploration of nanoscale morphologies of nanomaterials. This AI is developed by
a technique of visualizing intermediate activations of a Convolutional
AutoEncoder. In this method, a nano scale specimen image is transformed into
its feature representations by a Convolution Neural Network. The Convolutional
AutoEncoder is trained on 100% SEM dataset, 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 are widely
published in literature, there are not much publications that have visualized
Deep neural networks of nanomaterials. There is a significant opportunity 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 in Explainable AI in nano scale objects.
This paper contributes an open source AI with reproducible results at URL
(https://sites.google.com/view/aifornanotechnology)
