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Thursday, October 15, 2020 – 12:00PM to 1:00PM
Assistant Professor Tim Mueller
Title: Accelerating Materials Research Through Machine Learning
Location: Virtual Webex Address: https://duke.zoom.us/j/9088262425
Abstract: Machine learning has the potential to transform computational materials research by accelerating the calculation of material properties by orders of magnitude. I will present two examples of how this can be done at the atomic scale. In the first, I will demonstrate how machine learning, when combined with the cluster expansion approach, can be used to create highly accurate models of complex substitutional alloys. I will present several applications of this approach to problems in catalysis, including the prediction of the structures and properties of ternary alloy nanoparticles and the construction of novel catalytic activity maps of alloy phase diagrams. Such catalytic activity maps can be used to rapidly identify the synthesis conditions that are likely to produce highly stable and active catalysts, an important step towards rational catalysis design.