The recent development of electrochemical catalysis for small-molecule activation illustrated that the eventual reaction productivity and selectivity are the convoluted results from both catalysts' intrinsic activity and their structural morphology. As such a convolution complicates the engineering design of an electrocatalytic system, we would like to ask the following question: can we predict the best electrodes' nano/micro-structured morphology for a certain application demand given the intrinsic catalytic activity of a certain material? Here we would like to present an inverse design approach, enabled by machine learning, that addresses the above question.

Inverse design" of nano/micro-structured electrocatalytic electrodes based on machine learning

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.