Single-molecule neuromorphic device

Single-molecule neuromorphic device | 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 Single-molecule neuromorphic device Wenjing Hong, Hua Zhang, Jingyao Ye, Mingbin Gao, Chenshuai Yan, and 16 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7679516/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Artificial neural network-based machine learning provides foundations for artificial intelligence (AI), yet requires high energy costs for training. Beyond software-level simulation of neural networks, hardware-level implementation via neuromorphic devices becomes the next milestone in nanoscience towards energy-sustainable AI. Single-molecule devices have the potential for ultimate scale and energy efficiency, but challenges remain in achieving programmable multi-conductance states amidst room-temperature thermal fluctuations. Here we fabricate a bio-inspired single-molecule neuromorphic device consuming ~2.8 aJ/operation by electrochemically gating molecule-ion electrostatic interactions. This device realizes biomimetic emulation of neural plasticity from short-term to long-term memory featuring over 10 distinct conductance states, demonstrating the applications in Pavlovian conditioning for associative learning and pattern recognition in Morse code processing. Our approach enables multi-state synaptic emulation using an individual molecule toward energy-sustainable AI. Physical sciences/Nanoscience and technology/Nanoscale devices/Molecular electronics Physical sciences/Chemistry/Electrochemistry Physical sciences/Materials science/Nanoscale materials/Molecular machines and motors Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SinglemoleculeneuromorphicdeviceSI.docx SUPPLEMENTARY MATERIAL MovieS11.mp4 SUPPLEMENTARY MOVIE Cite Share Download PDF Status: Under Review 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. We do this by developing innovative software and high quality services for the global research community. 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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-7679516","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":533818816,"identity":"40555623-1c1d-498d-b07a-ae837de2ba25","order_by":0,"name":"Wenjing 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