This EAGER award from the National Science Foundation (NSF) Division of Materials Research supports an integrated computational-experimental approach to better understand the local atomic structure of disordered semiconductor materials. The $199,112 project, awarded to the Colorado School of Mines, focuses on the Ge(1-x)Mn(x)Te system as a model to explore how composition and processing conditions impact the local structure and, consequently, the electronic and thermal properties of these materials. The research utilizes advanced artificial intelligence techniques, specifically symmetry-aware neural networks, to model experimental neutron pair distribution function (PDF) measurements and visualize complex atomic-scale distortions. This project aims to enable more predictive relationships between semiconductor composition, processing, and functional properties, addressing key challenges in the field of disordered intermetallic materials. The work is funded under NSF's Mathematical and Physical Sciences (CFDA 47.049) program, which supports research to advance scientific understanding and strengthen the nation's scientific enterprise.
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