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Predicting?-decay energy with machine learning
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AI
DOI：10.1103/PhysRevC.107.034308.png)
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Abstract
En 中文
Qfl represents one of the most important factors characterizing unstable nuclei, as it can lead to a better understanding of nuclei behavior and the origin of heavy atoms. Recently, machine learning methods have been shown to be a powerful tool to increase accuracy in the prediction of diverse atomic properties such as energies, atomic charges, and volumes, among others. Nonetheless, these methods are often used as a black box not allowing unraveling insights into the phenomena under analysis. Here, the state-of-the-art precision of the fl-decay energy on experimental data is outperformed by means of an ensemble of machine-learning models. The explainability tools implemented to eliminate the black box concern allowed to identify proton and neutron numbers as the most relevant characteristics to predict Qfl energies. Furthermore, a physics-informed feature addition improved models' robustness and raised vital characteristics of theoretical models of the nuclear structure.
Keywords:
NEURAL-NETWORKS
IMPACT
Journal
IF:
3.4
Papers:
2.9W
Citations:
5.7W