Benchmarking Electro-organic Reaction Performance via Multidimensional Bayesian Optimization with Cross-phase Categorical-continuous Variable Sets

Benchmarking Electro-organic Reaction Performance via Multidimensional Bayesian Optimization with Cross-phase Categorical-continuous Variable Sets | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Benchmarking Electro-organic Reaction Performance via Multidimensional Bayesian Optimization with Cross-phase Categorical-continuous Variable Sets Mengning Ding, Luhan Dai, Pan Ran, Fangyuan Wang, Aoqian Qiu, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6734702/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Electrocatalysis and electrosynthesis serve as cornerstone technologies for clean energy-powered sustainable synthesis of value-added chemicals, pharmaceuticals, and fuels. However, the complex heterogeneous reaction pathways and vast interfacial chemical space demand a comprehensive understanding of experimental parameters for their efficient optimization towards full synthetic potentials – an aspect yet underexplored compared with extensive focus on catalyst design and screening. Further challenges arise from the high-performance demands across multiple catalytic dimensions, including yield, selectivity, energy efficiency, reaction rate, catalyst stability, whereas traditional trial-and-error approaches have been proven inefficient in navigating this vast chemical space. Here, we demonstrate Bayesian optimization as a powerful data-driven tool for effective multidimensional performance optimization in electro-organic reactions, enabled by joint employment of categorical and continuous variables to represent cross-interface chemical processes. For C(sp3)−H activation, mono-objective Bayesian optimization achieved benchmark performance in selective tetralin electro-oxidation (97% yield, >99% selectivity), while multi-objective strategies simultaneously enhanced yield/FE and enabled quantitative high-dimensional analysis. The framework versatility was validated through further performance breakthrough beyond the state-of-the-art investigations in olefin epoxidation and amine oxidation, where two- and three-dimensional performance metrics have been benchmarked, including boosted yield-FE from 80%-80% to 98%-91% for electro-epoxidation, and sixfold increase in reaction rate (corresponding current density from 9 mA/cm2 to 57 mA/cm2) for electro-amine oxidation. The success of Bayesian methodology across diverse reaction classes suggests a paradigm shift of electro-organic synthesis from empirical optimization to data-driven incremental gains, with new statistical insights into catalytic mechanisms and key impacting factors among the cross-phase chemical space. Physical sciences/Chemistry/Electrochemistry/Electrocatalysis Physical sciences/Engineering/Chemical engineering Physical sciences/Mathematics and computing/Information technology Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SI.pdf Supplementary Info Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6734702","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":482968875,"identity":"0ca49589-15bf-46a1-9ad5-49808fbd2b26","order_by":0,"name":"Mengning 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