Machine learning approach to pattern recognition in nuclear dynamics from the ab initio symmetry-adapted no-core shell model Article Swipe
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· 2022
· Open Access
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· DOI: https://doi.org/10.1103/physrevc.105.034306
· OA: W4214933544
A novel machine learning approach is used to provide further insight into atomic nuclei and to detect orderly patterns amid a vast data of large-scale calculations. The method utilizes a neural network that is trained on ab initio results from the symmetry-adapted no-core shell model (SA-NCSM) for light nuclei. We show that the SA-NCSM, which expands ab initio applications up to medium-mass nuclei by using dominant symmetries of nuclear dynamics, can reach heavier nuclei when coupled with the machine learning approach. In particular, we find that a neural network trained on probability amplitudes for s- and p-shell nuclear wave functions not only predicts dominant configurations for heavier nuclei but in addition, when tested for the <sup>20</sup>Ne ground state, accurately reproduces the probability distribution. The non-negligible configurations predicted by the network provide an important input to the SA-NCSM for reducing ultralarge model spaces to manageable sizes that can be, in turn, utilized in SA-NCSM calculations to obtain accurate observables. The neural network is capable of describing nuclear deformation and is used to track the shape evolution along the <sup>20-42</sup>Mg isotopic chain, suggesting a shape coexistence that is more pronounced toward the very neutron-rich isotopes. We provide first descriptions of the structure and deformation of <sup>24</sup>Si and <sup>40</sup>Mg of interest to x-ray burst nucleosynthesis, and even of the extremely heavy nuclei such as <sup>166,168</sup>Er and <sup>236</sup>U, that build on first-principles considerations.
