NucleiML: A machine learning framework of ground-state properties of finite nuclei for accelerated Bayesian exploration

[2504.03333] NucleiML: A machine learning framework of ground-state properties of finite nuclei for accelerated Bayesian exploration Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Nuclear Theory arXiv:2504.03333 (nucl-th) [Submitted on 4 Apr 2025 ( v1 ), last revised 9 Apr 2026 (this version, v2)] Title: NucleiML: A machine learning framework of ground-state properties of finite nuclei for accelerated Bayesian exploration Authors: Anagh Venneti , Chiranjib Mondal , Sk Md Adil Imam , Sarmistha Banik , Bijay K. Agrawal View a PDF of the paper titled NucleiML: A machine learning framework of ground-state properties of finite nuclei for accelerated Bayesian exploration, by Anagh Venneti and 4 other authors View PDF HTML (experimental) Abstract: The global behavior of the nuclear equation of state (EoS) is commonly studied using data from finite nuclei (FN), heavy-ion collisions, and astrophysical observations of neutron stars (NS). The constraints derived from FN such as binding energies and charge radii play the most crucial role in shaping the EoS up to saturation density. The computational cost associated with explicitly incorporating these constraints presents a significant challenge especially when the aim is to explore the model uncertainties rather than optimizing a single model. We address this by introducing NucleiML (NML), a machine learning framework trained on ground-state properties of a few finite nuclei generated by a relativistic mean-field model. NML allows us to integrate FN and NS properties within a Bayesian inference framework in an efficient manner. The results demonstrate reasonable accuracy and a speedup of $\sim 10^4$ times for calculation of FN properties for a single parameter set, yielding roughly $\sim 10^3 \times$ speed up in the Bayesian framework. The present study makes the case for extending the work to a larger set of nuclei, potentially enabling future studies of NS properties to incorporate the whole nuclear chart. Comments: 21 Pages, 11 figures Subjects: Nuclear Theory (nucl-th) ; High Energy Astrophysical Phenomena (astro-ph.HE) Cite as: arXiv:2504.03333 [nucl-th] (or arXiv:2504.03333v2 [nucl-th] for this version) https://doi.org/10.48550/arXiv.2504.03333 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Anagh Venneti [ view email ] [v1] Fri, 4 Apr 2025 10:29:06 UTC (470 KB) [v2] Thu, 9 Apr 2026 10:57:04 UTC (1,133 KB) Full-text links: Access Paper: View a PDF of the paper titled NucleiML: A machine learning framework of ground-state properties of finite nuclei for accelerated Bayesian exploration, by Anagh Venneti and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: nucl-th < prev | next > new | recent | 2025-04 Change to browse by: astro-ph astro-ph.HE References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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