Digital Twin for Chemical Sciences (DTCS): A Blueprint for Digitizing Chemical Characterization

Digital Twin for Chemical Sciences (DTCS): A Blueprint for Digitizing Chemical Characterization | 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 Digital Twin for Chemical Sciences (DTCS): A Blueprint for Digitizing Chemical Characterization Jin Qian, Asmita Jana, Siddarth Menon, Andrew Bogdan, Rebecca Hamlyn, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4870352/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Aug, 2025 Read the published version in Nature Computational Science → Version 1 posted You are reading this latest preprint version Abstract Directly visualizing chemical trajectories offers novel insights into catalysts, gas phase reactions, photo-induced dynamics, and quantum information processing. Identifying and tracking the exchange of matter to observe the creation and annihilation of chemical species is best achieved by closely coupling theory and experiment. We developed Digital Twin for Chemical Science (DTCS) v.01, a platform that mimics advanced characterization instruments, including those at Scientific User Facilities. DTCS v.01 addresses challenges in data acquisition, analysis, and model-driven interpretation via a physics-based, AI-accelerated approach. We validated this concept with ambient pressure X-ray Photoelectron Spectroscopy (APXPS) observations using a ubiquitous metal-water interfacial scenario, i.e., Ag/H2O, as a representative example. The inputs of DTCS v.01 are designed to mirror the experimental chemists' workflows, and the outputs can be directly compared to and are constantly updated from the experimental data. This integrated theoretical and experimental platform enhances user accessibility and facilitates the acquisition of standardized mechanistic insights. Physical sciences/Chemistry Physical sciences/Materials science Scientific community and society/Energy and society Full Text Additional Declarations There is NO Competing Interest. Supplementary Files DTSIv9.pdf Cite Share Download PDF Status: Published Journal Publication published 27 Aug, 2025 Read the published version in Nature Computational Science → 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-4870352","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":340815889,"identity":"37abc47d-d9a4-4106-9573-6bd112738989","order_by":0,"name":"Jin 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