84 citations · 1.1k across the 75 of their papers we have counts for
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Machine learning orbital-free density functional theory: taming quantum shell effects in deformed nuclei
X. H. Wu, Z. X. Ren, P. W. Zhao
Accurate description of deformed atomic nuclei by the orbital-free density functional theory has been a longstanding textbook challenge, due to the difficulty in accounting for the…
Multi-task learning on nuclear masses and separation energies with the kernel ridge regression
X. H. Wu, Y. Y. Lu, P. W. Zhao
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…
High-precision nuclear chronometer for the cosmos
X. H. Wu, P. W. Zhao, S. Q. Zhang +1
Nuclear chronometer, which predicts the ages of the oldest stars by comparing the present and initial abundances of long-lived radioactive nuclides, provides an independent dating…
Nuclear energy density functionals from machine learning
X. H. Wu, Z. X. Ren, P. W. Zhao
Machine learning is employed to build an energy density functional for self-bound nuclear systems for the first time. By learning the kinetic energy as a functional of the nucleon…
Two-dimensional collective Hamiltonian for chiral and wobbling modes II: Electromagnetic transitions
X. H. Wu, Q. B. Chen, P. W. Zhao +2
The intraband electromagnetic transitions in the framework of collective Hamiltonian for chiral and wobbling modes are calculated. By going beyond the mean field approximation on t…