{"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/inverse-design-of-crystals-using-generalized","title":"An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties","arxiv_id":"2005.07609","date":"2020-05-15","proceeding":null,"authors":["Zekun Ren","Siyu Isaac Parker Tian","Juhwan Noh","Felipe Oviedo","Guangzong Xing","Jiali Li","Qiaohao Liang","Ruiming Zhu","Armin G. Aberle","Shijing Sun","Xiaonan Wang","Yi Liu","Qianxiao Li","Senthilnath Jayavelu","Kedar Hippalgaonkar","Yousung Jung","Tonio Buonassisi"],"abstract":"Realizing general inverse design could greatly accelerate the discovery of new materials with user-defined properties. However, state-of-the-art generative models tend to be limited to a specific composition or crystal structure. Herein, we present a framework capable of general inverse design (not limited to a given set of elements or crystal structures), featuring a generalized invertible representation that encodes crystals in both real and reciprocal space, and a property-structured latent space from a variational autoencoder (VAE). In three design cases, the framework generates 142 new crystals with user-defined formation energies, bandgap, thermoelectric (TE) power factor, and combinations thereof. These generated crystals, absent in the training database, are validated by first-principles calculations. The success rates (number of first-principles-validated target-satisfying crystals/number of designed crystals) ranges between 7.1% and 38.9%. These results represent a significant step toward property-driven general inverse design using generative models, although practical challenges remain when coupled with experimental synthesis.","url_abs":"https://arxiv.org/abs/2005.07609v3","url_pdf":"https://arxiv.org/pdf/2005.07609v3.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":"inverse-design-of-crystals-using-generalized","repo_url":"https://github.com/PV-Lab/FTCP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2005.07609","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.07609"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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