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Wednesday, January 27, 2021 – 12:00PM to 1:00PM
Christopher A Sutton, PhD
Virtual Seminar: ZOOM LINK: https://duke.zoom.us/j/93319673112
Advances in machine learning (ML) are making a large impact in many disciplines, including materials and computational chemistry. A particularly exciting application of ML is the prediction of quantum mechanical (QM) properties (e.g., formation energy, bandgap, etc.) using only the composition or structure as input. Assuming sufficient accuracies in the ML models, these methods enable screening of a considerably large chemical space at orders of magnitude lower computational cost than QM methods. Despite the promise of ML in material screening, several key challenges remain in both applying and interpreting the results of ML algorithms.