5/16/2023 0 Comments Image similarityAlternatively, autoencoder structures have been leveraged to unmix statistical information from hyperspectral piezoresponse force microscopy 7 and current-voltage spectroscopy 8. To extract more information from images, CNNs have been extended using a UNET architecture to segment phases 5, and nanoparticles 6. Research tasks tend to emphasize unknown discoveries, and thus classification is ill-posed. However, classification is limited by the requirement that researchers know a piori what features they are looking to discover and have at least a small labeled dataset. For example, convolutional neural networks (CNNs) can be trained to identify imaging modes in electron microscopy 4. Most commonly, researchers have trained machine learning models on labeled datasets to identify pre-determined features of significance. There has been an emergence in machine learning tools to accelerate discoveries from imaging sources 3. Machine learning, however, is only as good as the objective with which it is trained and thus struggles with unstructured exploratory tasks 2. Recently, it has been purported that machine learning can extract and represent underlying physics from large datasets 1. While advances in optics and electronics have accelerated the resolution, length, and timescales of imaging, the downstream analysis tools have not kept pace. Instruments of scientific discovery from optical to scanning probe to electron microscopes and spectrometers generate enormous volumes of images containing information about the properties and structure of materials. While human memory has a tremendous capability to recall images, we cannot associate the images with metadata (e.g., date of data collection, sample properties, or sample processing and provenance, etc.). “Seeing is believing” thus, imaging is one of the most powerful tools for scientific discovery. We provide a customizable open-source package ( ) of this interactive tool for researchers to use with their data. This tool justifies continued investment in federated scientific databases with standardized metadata schemas where the combination of filtering and recursive interactive searching can uncover synthesis-structure-property relations. We demonstrate how this tool can be used for interactive recursive image searching and exploration, highlighting structural similarities at various length scales. As an exemplar, we use a set of 25,133 piezoresponse force microscopy images collected on diverse materials systems over five years. To improve these projections, we develop and train a model to include symmetry-aware features. Here, we develop a machine learning approach to create image similarity projections to search unstructured image databases. Moreover, there are no robust methods to search unstructured databases of images to deduce correlations and insight. Unfortunately, data from scientific experiments tend only to be accessible by the originator who knows the experiments and directives. One method to accelerate scientific discovery is to extract more insight from costly scientific experiments already conducted. In pursuit of scientific discovery, vast collections of unstructured structural and functional images are acquired however, only an infinitesimally small fraction of this data is rigorously analyzed, with an even smaller fraction ever being published.
0 Comments
Leave a Reply. |