Generalizing Denisov's daughter-deformation correction to DUR, AKRA, and New Geiger-Nuttall models produces improved alpha-decay half-life fits, with the modified AKRA version matching data best on 400 isotopes and yielding consistent but slightly higher predictions than ND for Z=118-124 nuclei at N
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Machine learning regression models applied to the two-potential approach incorporating alpha non-locality improve standard deviation by over 50% versus baseline TPA and generate half-life predictions for Z=118 and Z=120 even-even nuclei that are broadly consistent with established empirical formulas
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$\alpha$-decay systematics for superheavy nucleus: the effect of deformation of daughter nucleus
Generalizing Denisov's daughter-deformation correction to DUR, AKRA, and New Geiger-Nuttall models produces improved alpha-decay half-life fits, with the modified AKRA version matching data best on 400 isotopes and yielding consistent but slightly higher predictions than ND for Z=118-124 nuclei at N
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Nonlocality Effect in the Tunneling of Alpha Radioactivity with the Aid of Machine Learning
Machine learning regression models applied to the two-potential approach incorporating alpha non-locality improve standard deviation by over 50% versus baseline TPA and generate half-life predictions for Z=118 and Z=120 even-even nuclei that are broadly consistent with established empirical formulas