期刊
PHYSICAL REVIEW LETTERS
卷 127, 期 2, 页码 -出版社
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevLett.127.022502
关键词
-
资金
- U.S. Department of Energy, Office of Science, Office of Nuclear Physics [DE-AC02-06CH11357]
- NUCLEI SciDAC program
- Fermi Research Alliance, LLC [DEAC02-07CH11359]
- U.S. Department of Energy, Office of Science, Office of High Energy Physics
- DOE Office of Science [DE-AC0206CH11357]
In this study, a neural-network quantum state ansatz was introduced for modeling the ground-state wave function of light nuclei and solving the nuclear many-body Schrodinger equation efficiently. The approach successfully compared the ANN wave function with more conventional parametrizations and virtually exact Green's function Monte Carlo results.
The complexity of many-body quantum wave functions is a central aspect of several fields of physics and chemistry where nonperturbative interactions are prominent. Artificial neural networks (ANNs) have proven to be a flexible tool to approximate quantum many-body states in condensed matter and chemistry problems. In this work we introduce a neural-network quantum state ansatz to model the ground-state wave function of light nuclei, and approximately solve the nuclear many-body Schrodinger equation. Using efficient stochastic sampling and optimization schemes, our approach extends pioneering applications of ANNs in the field, which present exponentially scaling algorithmic complexity. We compute the binding energies and point-nucleon densities of A <= 4 nuclei as emerging from a leading-order pionless effective field theory Hamiltonian. We successfully benchmark the ANN wave function against more conventional parametrizations based on two- and three-body Jastrow functions, and virtually exact Green's function Monte Carlo results.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据