{"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/vmc-optimization-of-ultra-compact-explicitly","title":"VMC Optimization of Ultra-Compact, Explicitly-Correlated Wave Functions of the Li Isoelectronic Sequence in Its Lowest 1s2s2p Quartet State","arxiv_id":"2310.00916","date":"2023-10-02","proceeding":null,"authors":["D. J. Nader","B. M. Rubenstein"],"abstract":"A compact yet accurate approach for representing the wave functions of members of the He and Li isoelectronic series is using explicitly correlated wave functions. These wave functions, however, often have nonlinear forms, which make them challenging to optimize. In this work, we show how Variational Monte Carlo (VMC) can efficiently optimize explicitly correlated wave functions that accurately describe the quartet 1s2s2p state of the Li isoelectronic sequence with ten or fewer parameters. We find that our compact wave functions correctly describe cusp conditions and reproduce at least 99.9% percent of the exact energy.","url_abs":"https://arxiv.org/abs/2310.00916v1","url_pdf":"https://arxiv.org/pdf/2310.00916v1.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":"vmc-optimization-of-ultra-compact-explicitly","repo_url":"https://github.com/djuliannader/vmclitio","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}