https://doi.org/10.1140/epja/s10050-026-01835-2
Regular Article - Theoretical Physics
Nonlocality effect in the tunneling of alpha radioactivity with the aid of machine learning
Xingzhi College, Zhejiang Normal University, 321004, Jinhua, Zhejiang, China
a
This email address is being protected from spambots. You need JavaScript enabled to view it.
Received:
8
October
2025
Accepted:
10
March
2026
Published online:
30
March
2026
Abstract
Recently, building upon the research findings of Medeiros et al. (Phys Rev C 106:024608, 2022), we have extended the 
-particle nonlocality effect to the two-potential approach (TPA) (Hu and Wu in Eur Phys J A 61:129, 2025). This extension demonstrates that the integration of the -particle nonlocality effect into TPA yields relatively favorable results. In the present work, we employ machine learning methods to further optimize the aforementioned approach, specifically utilizing three classical machine learning models: decision tree regression, random forest regression, and XGBRegressor. Among these models, both the decision tree regression and XGBRegressor models exhibit the highest degree of agreement with the reference data, whereas the random forest regression model shows inferior performance. In terms of standard deviation, the results derived from the decision tree regression and XGBRegressor models represent improvements of 54.5% and 53.7%, respectively, compared to the TPA that does not account for the coordinate-dependent effective mass of particles. Furthermore, we extend the decision tree regression and XGBRegressor models to predict the -decay half-lives of 20 even–even nuclei with atomic numbers Z = 118 and Z = 120. Subsequently, the superheavy nucleus half-life predictions generated by our proposed models are compared with those from two established benchmarks: the improved eight-parameter Deng–Zhang–Royer (DZR) (Phys Rev C 101:034307, 2020) model and the new empirical expression (denoted as “New+D”) (Denisov in Phys Rev C 110:014604, 2024) proposed by V. Yu. Denisov, which explicitly incorporates nuclear deformation effects. Overall, the predictions from these models and formulas are generally consistent. Notably, the predictions of the decision tree regression model show a high level of consistency with those of the New+D expression, while the XGBRegressor model exhibits deviations from the other two comparative models.
Copyright comment Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Communicated by Haozhao Liang.
© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2026
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
