Kolmogorov-Arnold networks in nuclear binding energy prediction

Abstract:This study explores the application of Kolmogorov-Arnold networks (KANs) in predicting nuclear binding energies, leveraging their ability to decompose complex multiparameter systems into simpler univariate functions. By utilizing data from the Atomic Mass Evaluation (AME2020) and incorporating features such as atomic number, neutron number, and shell effects, KANs achieved a significant lower root mean square error (0.26 MeV), surpassing traditional models. The symbolic regression analysis yielded simplified analytical expressions for binding energies, aligning with classical models like the liquid drop model and the Bethe-Weizsaecker formula. These results highlight KANs' potential in enhancing the interpretability and understanding of nuclear phenomena, paving the way for future applications in nuclear physics and beyond.

Published version. 10 pages, 9 figures, 2 tables

Nuclear Theory (nucl-th); Machine Learning (cs.LG)

arXiv:2407.20737 [nucl-th]

(or arXiv:2407.20737v3 [nucl-th] for this version)

https://doi.org/10.48550/arXiv.2407.20737 Focus to learn more arXiv-issued DOI via DataCite

Phys. Rev. C 111, 024316 (2025)

https://doi.org/10.1103/PhysRevC.111.024316 Focus to learn more DOI(s) linking to related resources

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