Nuclear modeling is a long-standing challenge, owing to the complexity of nuclear interactions and the computational difficulty of solving quantum many-body problems. Density functional theory (DFT) and ab initio methods have made substantial progress, yet they are limited by distinct issues. Density functional theory suffers from the unknown exact form of the nuclear energy density functional, including both kinetic and interaction parts. The unknown exact form of kinetic energy density functional necessitates the use of auxiliary single-particle orbitals, i.e., the Kohn-Sham scheme. This elevates computational cost and restricts applications to heavy nuclear systems, such as superheavy nuclei and the inner crust of neutron stars. The lack of a known interaction functional leads to reliance on phenomenological constructions, which introduces model dependence and uncontrolled theoretical uncertainties. The ab initio methods face even more severe exponential computational scaling with increasing nucleon number, which limits their practical use to very light nuclear systems. The rapid development of data-driven methods, i.e., machine learning (ML) and artificial intelligence (AI), offers a new paradigm to address these issues. It has the potential to facilitate more efficient construction of energy density functionals and broaden the applicability of ab initio calculations.
This workshop aims to bring together nuclear physicists, computational scientists, ML researchers, and applied mathematicians from Asia, Europe, and the Americas to focus on frontier advances in data-driven nuclear modeling. It will highlight recent progress in machine-learning-assisted nuclear DFT and ab initio approaches, and also cross-field integration between electronic DFT and nuclear DFT. This meeting hopes to promote international collaboration, bridge nuclear physics with data science and electronic physics, and accelerate developments in nuclear structure, reactions, and astrophysical applications.
Organizers:
Pengwei Zhao (Co-chair, Peking University, China)
Yifei Niu (Co-chair, Shanghai Jiao Tong University, China)
Gianluca Colò (Università degli Studi di Milano, Italy)
Maria Piarulli (Washington University in Saint Louis, USA)
Chen Ji (Central China Normal University, China)
Sergei Manzhos (Institute of Science Tokyo, Japan)
Xin-Hui Wu (Fuzhou University, China)