Fig. 2
From: Learning surface molecular structures via machine vision
Workflow overview. a Block chart diagram of the workflow for classification of U and D states, and different rotational classes. b Graphical Markov model structure used for analysis of a molecular self-assembly of buckybowls. Here Markov network on a regular lattice acts as a prior over hidden variables (associated with the state of the molecule) in a model which is coupled to an array z of experimental observations (STM signal). c Schematics of convolutional neural network (cNN). The 12 convolutions of a size (21px × 21px) are generated by applying 12 kernels of a size (5px × 5px), with sigmoid activation function, to the input image. The filters are shifted across the image with a step size of 1px. These convolutions are subsampled into 12 maps of a size (7px × 7px) using average pooling technique. The second convolution layer is formed by applying 6 kernels, with sigmoid activation function, to an input from the previous layer. At the end of the network, a fully connected layers contains four neurons corresponding to different rotational classes
