Multi-objective Design of Multi-material Truss Lattices utilizing Graph Neural Networks

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Tucker, Mohamedreza Afrasiabi, Markus Bambach This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5362776/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract The rapid advancements in additive manufacturing (AM) across different scales and material classes have enabled the creationof architected materials with highly tailored properties. Beyond geometric flexibility, multi-material AM further expands designpossibilities by combining materials with distinct characteristics. While machine learning has recently shown great potentialfor the fast inverse design of lattice structures, its application has largely been limited to single-material systems. In thiswork, we propose a novel approach that incorporates material properties as edge features within the graph representation ofmulti-material truss lattices, utilizing graph neural networks (GNNs) to develop a fast and efficient inverse design framework.We validate this framework by designing lattices with tunable thermal expansion and stiffness properties, showcasing its abilityto explore a broad and flexible design space. We showcase the framework’s inverse design capabilities for both single andmulti-objective optimization tasks and assess its limitations. Additionally, we demonstrate the superior capacity of GNNsin capturing structure-property relationships for multi-material systems. We anticipate that the continued advancement ofGNN-assisted inverse design will play a key role in unlocking the full potential of multi-material truss lattices. Physical sciences/Engineering/Mechanical engineering Physical sciences/Mathematics and computing/Scientific data Physical sciences/Materials science/Theory and computation/Computational methods Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 25 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 03 Dec, 2024 Reviews received at journal 02 Dec, 2024 Reviews received at journal 23 Nov, 2024 Reviewers agreed at journal 22 Nov, 2024 Reviewers agreed at journal 22 Nov, 2024 Reviewers invited by journal 21 Nov, 2024 Editor assigned by journal 21 Nov, 2024 Editor invited by journal 17 Nov, 2024 Submission checks completed at journal 14 Nov, 2024 First submitted to journal 30 Oct, 2024 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-5362776","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":385534921,"identity":"9b013d41-4d31-4bff-a2a3-690822ff9863","order_by":0,"name":"Ramón Frey","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYJCCD0Asx8DAA2IfIEoH4wwgYczARqqWxAaitZi3n05srqg4nN4v33v4A0PNHcJaZM7kbmw8c+Zw7sw2vgQDhmPPCGuRYMjd/rCx7XDuhmM8BgmMDYeJ0ML/dmNj47/D6fZALQeI0yIBdFhjw+EEAzYewwYitQBtaTiWbjjjWI4xQ8IxohwGtKWhxlqev/mM8YcPNURogYJmCJVAtAYGhjoS1I6CUTAKRsGIAwCVpT6SI7tNtQAAAABJRU5ErkJggg==","orcid":"","institution":"ETH Zurich","correspondingAuthor":true,"prefix":"","firstName":"Ramón","middleName":"","lastName":"Frey","suffix":""},{"id":385534922,"identity":"2ab705ae-be62-4bfc-84b1-7c889c988ade","order_by":1,"name":"Michael R. 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