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

Understand

A novel machine learning approach is used to provide further insight into atomic nuclei and to detect orderly patterns amidst 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 $^{20}$Ne ground state, it accurately reproduces the probability distribution.