Advances in machine learning methods provide tools that have broad applicability in
scientific research. These techniques are being applied across the diversity of nuclear …

During the last few decades, rare isotope beam facilities have provided unique data for
studying the properties of nuclides located far from the beta-stability line. Such nuclei are …

We describe the Bayesian analysis of nuclear dynamics (BAND) framework, a
cyberinfrastructure that we are developing which will unify the treatment of nuclear models …

To improve the predictability of complex computational models in the experimentally-
unknown domains, we propose a Bayesian statistical machine learning framework utilizing …

Theoretical prediction shows that about 9000 nuclei could be bounded, of which the
properties will be hot topics in the new nuclear physics era opened by the new third …

A multi-task learning (MTL) framework, called gradient kernel ridge regression, for nuclear
masses and separation energies is developed by introducing gradient kernel functions to …

Background: The chart of the nuclides is limited by particle drip lines beyond which nuclear
stability to proton or neutron emission is lost. Predicting the range of particle-bound isotopes …

Background: The phenomenon of reflection-asymmetric nuclear shapes is relevant to
nuclear stability, nuclear spectroscopy, nuclear decays and fission, and the search for new …

One of characteristic phenomena for nuclei beyond the proton dripline is the simultaneous
emission of two protons (2p). The current status of our knowledge of this most recently …

Eigenvector continuation (EC) has been shown to accurately and efficiently reproduce
ground states for targeted sets of Hamiltonian parameters. It uses as variational basis …