Machine learning (ML) techniques are catalyzing an era of data-driven materials research, enabling new possibilities such as autonomous collection and processing of massive, atomic resolution electron microscopy data sets on the scale of millions or even hundreds of millions of atoms. However, developing ML models that can reliably handle new or changing experimental conditions in such large data sets remains challenging. We address this challenge by developing a cycle generative adversarial network (CycleGAN) for generating realistic simulated Scanning Transmission Electron Microscopy (STEM) images. The CycleGAN includes a novel reciprocal space discriminator, which learns the complicated, low and high spatial frequency information from experimental data and transfers this information to simulated images. We demonstrate that this CycleGAN can convert easily generated, but unrealistic, simulated data into realistic images that are nearly indistinguishable from experiment. Such images are valuable because they represent a new method to rapidly generate labeled, experimentally realistic training data for ML-based image analysis—thereby addressing a major barrier that has previously limited the accuracy, ease-of-use, and generalizability of ML in materials characterization. These results represent an important step towards autonomous, large-scale data collection and processing of materials characterization data.
Pinshane Huang is an associate professor in the Department of Materials Science and Engineering at the University of Illinois Urbana-Champaign. She holds a B.A. in Physics from Carleton College and a Ph.D. in Applied Physics from Cornell University. Her current research is focused around transmission electron microscopy and spectroscopy of two-dimensional materials and soft-hard interfaces. Her work has produced iconic images showing how defects occur in atomically thin materials such as graphene, 2D semiconductors, and silica glass. Her awards include a Presidential Early Career Award for Scientists and Engineers (PECASE), a Packard Fellowship, a Sloan Fellowship, and young investigator awards from the Department of Defense and the National Science Foundation. Her research has been featured in National Geographic, BusinessWeek, CBS News, Discover Magazine, and the Guinness Book of World Records.
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