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We present a workflow for obtaining fully trained artificial neural networks that can perform automatic particle segmentations of agglomerated, non-spherical nanoparticles from electron microscopy images “from scratch”, without the need for large training data sets of manually annotated images. This is achieved by using unsupervised learning for most of the training dataset generation, making heavy use of generative adversarial networks and especially unpaired image-to-image translation via cycle-consistent adversarial networks. The whole process only requires about 15 minutes of hands-on time by a user and can typically be finished within less than 12 hours when training on a single graphics card (GPU). After training, SEM image analysis can be carried out by the artificial neural network within seconds, and the segmented images can be used for automatically extracting and calculating various other particle size and shape descriptors.
Researchers study the properties of polymeric membranes - an important building block of batteries – by first measuring its impedance spectra and then determining a so called equivalent electronic circuit (EEC), that is an electronic circuit that approximately reproduces these measurements. Determining EEC is not completely automatized since it includes the search for the topology of the electronic circuit. This search is an inverse problem, since the formulas for determining the impedance spectra given the circuit topology and parameters are known while there are no formulas for the invers direction. To solve this invers problem, the original problem is split into two parts. The first part is to determine the topology of the circuit and the second part is to determine the parameters of the circuit. For the first part a Convolutional Neural Network Classifier is trained on a simulated data set, where the simulator is an implementation of the formulas for determining the impedance spectra given the complete description of the electronic circuit. After the topology is determined, the parameters of the electronic circuit are found by minimizing the error between the observed spectra and the spectra corresponding to these parameters. This minimization is a global optimization.