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Here, the MRSEC team at the University of Tennessee, Knoxville presents a hybrid machine learning (ML) framework that integrates a physically constrained variational autoencoder (pc-VAE) with different Bayesian optimization (BO) methods to systematically accelerate and improve crystal structure refinement, from traditional least-square based refinement, with resolution of defects.

Through “Mission: Materials Science,” a long-running outreach initiative supported by Penn State’s U.S. National Science Foundation-funded Materials Research Science and Engineering Center (MRSEC), graduate students from across disciplines have partnered with museum educators and media professionals to transform materials science research into free, hands-on learning activities. The newest collection, recently launched online, adds four experiments designed for children ages 8 to 13.

When Allan H. MacDonald, a theoretical physicist at The University of Texas at Austin and a faculty investigator in UT Austin’s Center for Dynamics and Control of Materials, an NSF MRSEC, began exploring two-dimensional materials decades ago, the field was still taking shape. Today, that work has helped define an entirely new area of nanoscience — and earned him one of science’s major international honors.