ABSTRACT

With support from the Chemical Catalysis (CAT) and Chemical Structure, Dynamics, and Mechanisms-B (CSDM-B) programs in the Division of Chemistry, the collaborative team of Chong Liu and Quanquan Gu of the University of California, Los Angeles and Jenny Y. Yang of the University of California, Irvine is working to establish an electrochemical research automation platform that requires minimal human intervention. Successful completion of this project will showcase the feasibility, caveats, and power of autonomous electrochemical research and promise a paradigm shift in how scientific research investigation in electrochemistry and electrocatalysis will be conducted. The project also introduces the opportunity of training the next-generation researchers and workforce with diverse skill sets in an interdisciplinary research environment. The software and methodology developed will be made publicly accessible free of charge and incorporated into an educational boot camp focused on electrochemistry. A boot camp on electrochemistry, automation, and artificial intelligence for undergraduate, graduate, and postdoctoral participants, particularly those from socio-economically underrepresented groups, will be established. This boot camp will engage senior-level undergraduate and graduate students, as well as postdoctoral scholars, will foster interdisciplinarity and will help to build an AI-savvy chemistry workforce.

Under this award, the tripartite collaborative of Chong Liu and Quanquan Gu of the University of California, Los Angeles and Jenny Y. Yang of the University of California, Irvine are establishing a proof-of-concept platform to autonomously conduct electrochemistry research with high throughput and at least partly supplement, if not replace, the manual process. The team will develop algorithms based on deep learning to automatically analyze electrochemical data and construct an experimentation platform for mechanistic studies of proton-coupled electron transfer in electrochemistry. Specifically, the aims of this proposal are: (1) to develop automatic algorithms based on deep learning that automatically analyze cyclic voltammograms as a classic example of electrochemical data; (2) to construct an autonomous experimentation platform that automates electrochemical testing and iteratively designs new experiments based on the group's understanding of the deep-learning algorithm and Bayesian optimization and (3) to employ the established platform to conduct mechanistic studies of proton-coupled electron transfer (PCET) in electrochemistry and discover new reactivities in homogenous electrocatalysis of CO2 fixation.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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