Low-dimensional uniform manifold approximation projection showing symmetry-aware image similarity from a database of greater than 25,000 piezoresponse force microscopy images. Credit: Joshua Agar/Lehigh University A novel neural network to understand symmetry, speed materials research. Using a large, unstructured dataset gleaned from 25,000 images, scientists demonstrate a novel machine learning technique to identify structural similarities and trends in materials for the first time. Understanding structure-property relations is a key goal of materials research, according to Joshua Agar, a faculty member in Lehigh University’s Department of Materials Science and Engineering. And yet currently no metric exists to understand the structure of materials because of the complexity and multidimensional nature of structure. Artificial neural networks, a type of machine learning, can be trained to identify similarities―and even correlate parameters such as structure and properties―but the...