lizhilong
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I-shapedSpecialist — deep expertise in Python
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α-decay energies were predicted using machine learning techniques
This project investigates the systematics of nuclear yrast and yrare 2⁺ excited states using data-driven machine learning methods. By analyzing correlations between nuclear structure properties and excitation energies 𝐸 ( 2 1 + ) E(2 1 + ) and 𝐸 ( 2 2 + ) E(2 2 + ), the model aims to uncover underlying physical patterns .
We used SVGP and LightGBM to predict the nuclear charge radii formore than 3000 nuclei, providing the predicted values and the corresponding errors.
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