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 there are two major challenges, says Agar. One is that the majority of the vast amounts of data generated by materials experiments are never analyzed. This is largely because such images, produced by scientists in laboratories all over the world, are rarely stored in a usable manner and not usually shared with other research teams. The second challenge is that neural networks are not very effective at learning symmetry and periodicity (how periodic a material’s structure is), two features of utmost importance to materials researchers.
Now, a team led by Lehigh University has developed a novel machine learning approach that can create similarity projections via machine learning, enabling researchers to search an unstructured image database for the first time and identify trends. Agar and his collaborators developed and trained a neural network model to include symmetry-aware features and then applied their method to a set of 25,133 piezoresponse force microscopy images collected on diverse materials systems over five years at the University of California, Berkeley. The results: they were able to group similar classes of material together and observe trends, forming a basis by which to start to understand structure-property relationships.
“One of the novelties of our work is that we built a special neural network to understand symmetry and we use that as a feature extractor to make it much better at understanding images,” says Agar, a lead author of the paper where the work is described: “Symmetry-Aware Recursive Image Similarity Exploration for Materials Microscopy,” published October 8 in Nature Computational Materials Science. In addition to Agar, authors include, from Lehigh University: Tri N. M. Nguyen, Yichen Guo, Shuyu Qin and Kylie S. Frew and, from Stanford University: Ruijuan Xu. Nguyen, a lead author, was an undergraduate at Lehigh University and is now pursuing a PhD at Stanford.
Story by Lori Friedman
