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...

Shiny nacre of Abalone washed ashore.  Strongest and toughest glass known developed by McGill University scientists. Scientists from McGill University develop stronger and tougher glass, inspired by the inner layer of mollusk shells. Instead of shattering upon impact, the new material has the resiliency of plastic and could be used to improve cell phone screens in the future, among other applications. While techniques like tempering and laminating can help reinforce glass, they are costly and no longer work once the surface is damaged. “Until now there were trade-offs between high strength, toughness, and transparency. Our new material is not only three times stronger than the normal glass, but also more than five times more fracture-resistant,” says Allen Ehrlicher, an Associate Professor in the Department of Bioengineering at McGill University. (A) Glass composite (without index-matching strategy on left and with index-matching on right), (B) Glass composite’s microstructure, (C)...