# Discovering Nuclear Models from Symbolic Machine Learning Source: [Discovering Nuclear Models from Symbolic Machine Learning]() Collection index: [llms.txt]() Data updated: 2025-10-25T07:42:50.580Z Authors: Jose M. Munoz, Silviu M. Udrescu, Ronald F. Garcia Ruiz Published: 2024-04-17T15:32:58.000Z ## Abstract Numerous phenomenological nuclear models have been proposed to describe specific observables within different regions of the nuclear chart. However, developing a unified model that describes the complex behavior of all nuclei remains an open challenge. Here, we explore whether novel symbolic Machine Learning (ML) can rediscover traditional nuclear physics models or identify alternatives with improved simplicity, fidelity, and predictive power. To address this challenge, we developed a Multi-objective Iterated Symbolic Regression approach that handles symbolic regressions over multiple target observables, accounts for experimental uncertainties and is robust against high-dimensional problems. As a proof of principle, we applied this method to describe the nuclear binding energies and charge radii of light and medium mass nuclei. Our approach identified simple analytical relationships based on the number of protons and neutrons, providing interpretable models with precision comparable to state-of-the-art nuclear models. Additionally, we integrated this ML-discovered model with an existing complementary model to estimate the limits of nuclear stability. These results highlight the potential of symbolic ML to develop accurate nuclear models and guide our description of complex many-body problems. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Nuclear mass excess prediction on AME Experiment I 2020 (interpolation)](): Nuclear mass excess prediction; AME Experiment I 2020 (interpolation); IntRMSE (MeV): 0.99. ## Original sources and implementation [Original paper]() [PDF]()