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Explores breaking linear scaling relationships in catalysis through strategies like controlling ensembles, using ligands, and introducing complexities to enhance performance.
Delves into the complementary methodologies of discrete choice and machine learning, covering notations, variables, models, data processes, extrapolation, what-if analysis, and more.
Explores the development of data science, education initiatives, and challenges in bridging the gap between data scientists and domain experts at EPFL.
