Spin-symmetry-enforced solution of the many-body Schrödinger equation with a deep neural network

IF 12 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Zhe Li, Zixiang Lu, Ruichen Li, Xuelan Wen, Xiang Li, Liwei Wang, Ji Chen, Weiluo Ren
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引用次数: 0

Abstract

The integration of deep neural networks with the variational Monte Carlo (VMC) method has marked a substantial advancement in solving the Schrödinger equation. In this work we enforce spin symmetry in the neural-network-based VMC calculation using a modified optimization target. Our method is designed to solve for the ground state and multiple excited states with target spin symmetry at a low computational cost. It predicts accurate energies while maintaining the correct symmetry in strongly correlated systems, even in cases in which different spin states are nearly degenerate. Our approach also excels at spin–gap calculations, including the singlet–triplet gap in biradical systems, which is of high interest in photochemistry. Overall, this work establishes a robust framework for efficiently calculating various quantum states with specific spin symmetry in correlated systems. An efficient approach is developed to enforce spin symmetry for neural network wavefunctions when solving the many-body Schrödinger equation. This enables accurate and spin-pure simulations of both ground and excited states.

Abstract Image

用深度神经网络求解多体Schrödinger方程的自旋对称强制解。
深度神经网络与变分蒙特卡罗(VMC)方法的集成在求解Schrödinger方程方面取得了实质性进展。在这项工作中,我们使用改进的优化目标在基于神经网络的VMC计算中增强自旋对称性。我们的方法旨在以较低的计算成本求解具有目标自旋对称性的基态和多激发态。它预测了精确的能量,同时在强相关系统中保持了正确的对称性,即使在不同的自旋态几乎是简并的情况下也是如此。我们的方法也擅长于自旋间隙计算,包括双基系统中的单线态-三重态间隙,这在光化学中具有很高的兴趣。总的来说,这项工作为有效计算相关系统中具有特定自旋对称性的各种量子态建立了一个健壮的框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
11.70
自引率
0.00%
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