论文标题
解决核结构问题的深神经网络方法
Deep-neural-network approach to solving the ab initio nuclear structure problem
论文作者
论文摘要
从量子力学的第一原理中预测量子多体系统的结构是物理，化学和材料科学的常见挑战。事实证明，深度机器学习是解决凝结物质和化学问题的强大工具，而对于原子核来说，由于核子核子的复杂相互作用，它仍然很具有挑战性，这些核素相互作用强烈地融入了空间，旋转和同胞的自由度。通过结合核波函数的基本物理和人工神经网络的强表达能力，我们开发了feynmannet，这是一种深入学习的变化变异量子蒙特卡洛方法，用于\ emph {ab intib}核结构。我们表明，Feynmannet可以为$^4 $ HE，$^6 $ li提供非常准确的解决方案和波浪功能，甚至最高$^{16} $ o从领先的和近代的领先阶级汉密尔顿汉密尔顿汉密尔顿人出现。与常规的蒙特卡洛方法相比，遭受了严重的固有费米亚签名问题，Feynmannet以各种方式达到了如此高的精度，并与核子数量多样地缩放。因此，它铺平了基于核子之间现实的相互作用来预测核性质的高度准确有效\ emph {ab intio}方法的道路。
Predicting the structure of quantum many-body systems from the first principles of quantum mechanics is a common challenge in physics, chemistry, and material science. Deep machine learning has proven to be a powerful tool for solving condensed matter and chemistry problems, while for atomic nuclei it is still quite challenging because of the complicated nucleon-nucleon interactions, which strongly couple the spatial, spin, and isospin degrees of freedom. By combining essential physics of the nuclear wave functions and the strong expressive power of artificial neural networks, we develop FeynmanNet, a deep-learning variational quantum Monte Carlo approach for \emph{ab initio} nuclear structure. We show that FeynmanNet can provide very accurate solutions of ground-state energies and wave functions for $^4$He, $^6$Li, and even up to $^{16}$O as emerging from the leading-order and next-to-leading-order Hamiltonians of pionless effective field theory. Compared to the conventional diffusion Monte Carlo approaches, which suffer from the severe inherent fermion-sign problem, FeynmanNet reaches such a high accuracy in a variational way and scales polynomially with the number of nucleons. Therefore, it paves the way to a highly accurate and efficient \emph{ab initio} method for predicting nuclear properties based on the realistic interactions between nucleons.
