Quantum mechanics

Deformation (meteorology)

Algorithm

Statistics

Nuclear structure

Nuclear physics

Energy (signal processing)

Atomic physics

Set (abstract data type)

Computer science

Chart

Statistical physics

Physics

Mathematics

Artificial intelligence

Excited state

Programming language

Meteorology

Artificial neural network

Machine learning

State (computer science)

Physical Review Letters

We demonstrate that a committee of deep neural networks is capable of predicting the ground-state and excited energies of more than 1800 atomic nuclei with an accuracy akin to the one achieved by state-of-the-art nuclear energy density functionals (EDFs) and with significantly less computational cost. An active learning strategy is proposed to train this algorithm with a minimal set of 210 nuclei. This approach enables future fast studies of the influence of EDF parametrizations on structure properties over the whole nuclear chart and suggests that for the first time a machine learning framework successfully encoded several correlated aspects of nuclear deformation.Received 9 October 2019Revised 7 January 2020Accepted 18 March 2020DOI:https://doi.org/10.1103/PhysRevLett.124.162502© 2020 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasNuclear density functional theoryNuclear structure & decaysTechniquesMachine learningNeuronal network modelsNuclear Physics

Taming Nuclear Complexity with a Committee of Multilayer Neural Networks