{"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/general-purpose-machine-learned-potential-for","title":"General-purpose machine-learned potential for 16 elemental metals and their alloys","arxiv_id":"2311.04732","date":"2023-11-08","proceeding":null,"authors":["Keke Song","Rui Zhao","Jiahui Liu","Yanzhou Wang","Eric Lindgren","Yong Wang","Shunda Chen","Ke Xu","Ting Liang","Penghua Ying","Nan Xu","Zhiqiang Zhao","Jiuyang Shi","Junjie Wang","Shuang Lyu","Zezhu Zeng","Shirong Liang","Haikuan Dong","Ligang Sun","Yue Chen","Zhuhua Zhang","Wanlin Guo","Ping Qian","Jian Sun","Paul Erhart","Tapio Ala-Nissila","Yanjing Su","Zheyong Fan"],"abstract":"Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their applicability. Here, we present a feasible approach for constructing a unified general-purpose MLP for numerous elements, demonstrated through a model (UNEP-v1) for 16 elemental metals and their alloys. To achieve a complete representation of the chemical space, we show, via principal component analysis and diverse test datasets, that employing one-component and two-component systems suffices. Our unified UNEP-v1 model exhibits superior performance across various physical properties compared to a widely used embedded-atom method potential, while maintaining remarkable efficiency. We demonstrate our approach's effectiveness through reproducing experimentally observed chemical order and stable phases, and large-scale simulations of plasticity and primary radiation damage in MoTaVW alloys. This work represents a significant leap towards a unified general-purpose MLP encompassing the periodic table, with profound implications for materials science.","url_abs":"https://arxiv.org/abs/2311.04732v2","url_pdf":"https://arxiv.org/pdf/2311.04732v2.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"general-purpose-machine-learned-potential-for","repo_url":"https://github.com/brucefan1983/GPUMD","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"general-purpose-machine-learned-potential-for","repo_url":"https://github.com/jonsnow-willow/gpumd-wizard","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"AGPL-3.0"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2311.04732","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.04732"}},"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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