{"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/physics-guided-generative-adversarial-1","title":"Physics Guided Deep Learning for Generative Design of Crystal Materials with Symmetry Constraints","arxiv_id":"2203.14352","date":"2022-03-27","proceeding":null,"authors":["Yong Zhao","Edirisuriya M. Dilanga Siriwardane","Zhenyao Wu","Nihang Fu","Mohammed Al-Fahdi","Ming Hu","Jianjun Hu"],"abstract":"Discovering new materials is a challenging task in materials science crucial to the progress of human society. Conventional approaches based on experiments and simulations are labor-intensive or costly with success heavily depending on experts' heuristic knowledge. Here, we propose a deep learning based Physics Guided Crystal Generative Model (PGCGM) for efficient crystal material design with high structural diversity and symmetry. Our model increases the generation validity by more than 700\\% compared to FTCP, one of the latest structure generators and by more than 45\\% compared to our previous CubicGAN model. Density Functional Theory (DFT) calculations are used to validate the generated structures with 1,869 materials out of 2,000 are successfully optimized and deposited into the Carolina Materials Database \\url{www.carolinamatdb.org}, of which 39.6\\% have negative formation energy and 5.3\\% have energy-above-hull less than 0.25 eV/atom, indicating their thermodynamic stability and potential synthesizability.","url_abs":"https://arxiv.org/abs/2203.14352v3","url_pdf":"https://arxiv.org/pdf/2203.14352v3.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":"physics-guided-generative-adversarial-1","repo_url":"https://github.com/MilesZhao/PGCGM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"formation-energy","task_name":"Formation Energy"}],"methods":[{"method_slug":"base","method_name":"BASE"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2203.14352","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.14352"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/MilesZhao/PGCGM","reach":null}],"summary":{"ran_fixture":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"47dd2b755ba57b84","entry":"calc_grad_penalty","repo":"MilesZhao/PGCGM","repo_kind":"official","path":"model.py","file_url":"https://github.com/MilesZhao/PGCGM/blob/HEAD/model.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"47dd2b755ba57b84"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}