Predicting Mechanical Properties of Non-Equimolar High-Entropy Carbides using Machine Learning

Predicting Mechanical Properties of Non-Equimolar High-Entropy Carbides using Machine Learning | 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 Predicting Mechanical Properties of Non-Equimolar High-Entropy Carbides using Machine Learning Xi Zhao, Xi Zhao, Shuguang Cheng, Shuguang Cheng, Sen Yu, Sen Yu, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4308682/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 High-entropy carbides (HECs) have garnered significant attention due to their unique mechanic properties. However, the design of novel HECs has been limited by extensive trial-and-error strategies, along with insufficient knowledge and computational capabilities. In this work, the intrinsic correlations between elements in the high-dimensional compositional space of HECs are investigated using high-throughput density functional theory calculations and two machine learning models, which enable us to predict the Young's modulus, hardness and wear resistance only using a chemical formula. Our models demonstrate low root mean square error (11.5GPa) and mean absolute errors (9.0GPa) in predicting the mechanic properties of HECs with arbitrary non-equimolar compositions. We further establish a database of 566,370 HECs and identified 15 novel HECs with best mechanical properties. Our models can rapidly explore the mechanical properties of HECs with descriptor-property correlation analysis, and hence provide an efficient method for accelerating the design of non-equimolar high-entropy materials with desired performance. Physical sciences/Materials science/Structural materials/Ceramics Physical sciences/Materials science/Theory and computation Physical sciences/Materials science/Structural materials/Ceramics Physical sciences/Materials science/Theory and computation High-entropy carbides (HECs) High-throughput density functional theory calculation (HT-DFT) Machine Learning (ML) Mechanical properties Full Text Additional Declarations (Not answered) Supplementary Files SupplementaryInformation.pdf 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. 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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-4308682","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":298272006,"identity":"9764a63b-b88c-4bed-a228-5304bcb0d8e9","order_by":0,"name":"Xi 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