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NSR database version of April 29, 2024.

Search: Author = O.M.Molchanov

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2022MO10      Phys.Rev. C 105, 034306 (2022)

O.M.Molchanov, K.D.Launey, A.Mercenne, G.H.Sargsyan, T.Dytrych, J.P.Draayer

Machine learning approach to pattern recognition in nuclear dynamics from the ab initio symmetry-adapted no-core shell model

NUCLEAR STRUCTURE ⁴He, ¹⁶O, ²⁰Ne, ²⁴Si, ^{20,22,24,26,28,30,32,34,36,38,40,42}Mg, ^166,168Er, ²³⁶U; calculated probability amplitudes of dominant configurations for ground states, shape coexistence and structure patterns using machine learning on ab initio symmetry-adapted no-core shell model calculations. Neural networks with training sets that include only the s- and p-shell nuclei.

doi: 10.1103/PhysRevC.105.034306
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