{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/microstructure-representation-and","title":"Microstructure Representation and Reconstruction of Heterogeneous Materials via Deep Belief Network for Computational Material Design","arxiv_id":"1612.07401","date":"2016-12-22","proceeding":null,"authors":["Ruijin Cang","Yaopengxiao Xu","Shaohua Chen","Yongming Liu","Yang Jiao","Max Yi Ren"],"abstract":"Integrated Computational Materials Engineering (ICME) aims to accelerate\noptimal design of complex material systems by integrating material science and\ndesign automation. For tractable ICME, it is required that (1) a structural\nfeature space be identified to allow reconstruction of new designs, and (2) the\nreconstruction process be property-preserving. The majority of existing\nstructural presentation schemes rely on the designer's understanding of\nspecific material systems to identify geometric and statistical features, which\ncould be biased and insufficient for reconstructing physically meaningful\nmicrostructures of complex material systems. In this paper, we develop a\nfeature learning mechanism based on convolutional deep belief network to\nautomate a two-way conversion between microstructures and their\nlower-dimensional feature representations, and to achieves a 1000-fold\ndimension reduction from the microstructure space. The proposed model is\napplied to a wide spectrum of heterogeneous material systems with distinct\nmicrostructural features including Ti-6Al-4V alloy, Pb63-Sn37 alloy,\nFontainebleau sandstone, and Spherical colloids, to produce material\nreconstructions that are close to the original samples with respect to 2-point\ncorrelation functions and mean critical fracture strength. This capability is\nnot achieved by existing synthesis methods that rely on the Markovian\nassumption of material microstructures.","url_abs":"http://arxiv.org/abs/1612.07401v3","url_pdf":"http://arxiv.org/pdf/1612.07401v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"microstructure-representation-and","repo_url":"https://github.com/DesignInformaticsLab/Material-Design","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"}],"methods":[{"method_slug":"deep-belief-network","method_name":"Deep Belief Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}