From Complexity to Clarity: Kolmogorov-Arnold Networks in Nuclear Binding Energy Prediction

2024/07/30 by Hao Liu, Liu, Hao, Lei, Jin +2
Materials Science · #FOS: Physical sciences #Machine Learning in Materials Science #Nuclear Theory (nucl-th)

paper · pdf · doi:10.48550/arxiv.2407.20737

openalex publication_date 2024/07/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

This study explores the application of Kolmogorov-Arnold Networks (KANs) in predicting nuclear binding energies, leveraging their ability to decompose complex multi-parameter 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-Weizsäcker 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.

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