arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 2603.14775cond-mat.str-elcond-mat.dis-nnphysics.comp-phquant-ph
收藏

神经网络反向流用于从头计算固体材料

Neural network backflow for ab-initio solid calculations

University of Illinois at Urbana-Champaign(伊利诺伊大学厄巴纳-香槟分校)

An-Jun Liu, Bryan K. Clark

AI总结:

本文提出基于神经网络反向流的方法,用于从头计算固体材料,通过两阶段剪枝策略提升效率,实现高精度模拟,验证了其在氢链、石墨烯和硅晶体中的有效性。

摘要:

准确模拟扩展周期系统是凝聚态物理的核心挑战。神经量子态(NQS)提供了此类任务的表达波函数,但面临可扩展性问题。本文成功将神经网络反向流(NNBF)方法扩展到从头计算固态材料。基于我们为分子设计的可扩展优化框架[1],我们引入了两阶段剪枝策略来管理巨大的配置空间扩展:通过利用计算成本低且物理信息指导的重要性代理,我们仅将精确的NNBF振幅评估应用于最相关的决定性,显著提高了优化效率、能量估计和收敛性。我们的框架在多种固态基准上实现了最先进的准确性。对于1D氢链,NNBF与DMRG和AFQMC匹配或优于,且在强相关键断裂区域保持稳健,而耦合簇方法失效。它能平滑地外推到热力学极限。我们进一步通过计算二维石墨烯和三维硅的基态势能曲线验证了其可扩展性。最后,消融研究验证了我们剪枝策略的计算节省,并突显了NNBF能量对基组的依赖性。

英文摘要:

Accurately simulating extended periodic systems is a central challenge in condensed matter physics. Neural quantum states (NQS) offer expressive wavefunctions for this task but face issues with scalability. In this work, we successfully extend the neural network backflow (NNBF) approach to ab-initio solid-state materials. Building on our scalable optimization framework for molecules [Liu et al., PRB 112, 155162 (2025)], we introduce a two-stage pruning strategy to manage the massive configuration space expansions: by utilizing a computationally cheap, physics-informed importance proxy, we devote exact NNBF amplitude evaluations solely to the most relevant determinants, significantly improving optimization efficiency, energy estimation, and convergence. Our framework achieves state-of-the-art accuracy across diverse solid-state benchmarks. For 1D hydrogen chains, NNBF matches or surpasses DMRG and AFQMC, remains robust in strongly correlated bond-breaking regimes where coupled-cluster methods fail, and smoothly extrapolates to the thermodynamic limit. We further demonstrate its scalability by computing ground-state potential energy curves for 2D graphene and 3D silicon. Finally, ablation studies validate the computational savings of our pruning strategy and highlight the dependence of the NNBF energies on basis sets.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑