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| Format: | Recurso digital |
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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.19559859 |
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Table of Contents:
- <p>Email contact : multiversou@gmail.com </p> <p>We present a minimalist Mixture Density Network (MDN) for nuclear mass predictions using only 12 bulk‑inspired features, excluding explicit shell corrections, Wigner terms, and deformation parameters. Trained on AME2016, the model achieves 0.38 MeV RMS for nuclei with A ≥ 60—surpassing FRDM2012 on the same subset—while failing systematically for A < 60 (RMS ≈ 2 MeV). This sharp transition identifies A ≈ 60 as the boundary between statistically learnable mean‑field behavior and quantum‑correlation‑dominated regimes. Major shell closures emerge implicitly from the data, whereas light N = Z nuclei reveal missing Wigner energy and clustering effects. The work provides a transparent, calibrated baseline for hybrid physics–machine‑learning approaches and defines the domain of validity for physics‑minimal models.</p>