Research

Chemical conversions enabled by catalysis impact many areas including living systems, energy conversion, agriculture, environmental protection, etc. The chemical transformation process is associated with dynamic structural rearrangements in the catalysts. Characterizing structural dynamics from a large and complex dataset is very challenging. Therefore, we propose a completely new approach based on the data science approach, including denoising, clustering, dynamic Markovian models, as well as physical simulations.

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