arXivDaily arXiv每日学术速递 周一至周五更新
稍安勿躁,官方尚未更新
arXiv 2302.01099cond-mat.str-elphysics.comp-phquant-ph
收藏

基于高斯过程态的高效从头算电子结构框架

A framework for efficient ab initio electronic structure with Gaussian Process States

Yannic Rath, George H. Booth

AI总结:

提出基于高斯过程态的通用框架,通过局部福克空间表示实现高效随机采样,在64电子体系及三维氢莫特相变模型中达到竞争精度,显著优于现有方法。

摘要:

我们提出了一个通用框架,用于利用现代机器学习启发的量子多体态表示,高效模拟真实费米子系统,旨在成为从头算电子结构的通用工具。这些机器学习启发的拟设最近在(第一量子化的)连续表示和离散福克空间表示中崭露头角,然而,后者在处理真实相互作用时固有的标度问题迄今限制了其实际应用。我们以“高斯过程态”为例——这是一种近期提出的、受机器学习中可系统改进的核模型启发的拟设——讨论了定义计算福克空间表示的不同选择。我们展示了局部表示特别适合期望值的随机采样,同时指出了克服与连续模型相比标度差异的途径。我们展示了对于多达64个电子的系统具有竞争力的精度,包括三维氢中莫特相变的简化(但完全从头算)模型,表明即使在中等数量的构型样本下,该方法也显著优于类似方法。

英文摘要:

We present a general framework for the efficient simulation of realistic fermionic systems with modern machine learning inspired representations of quantum many-body states, towards a universal tool for ab initio electronic structure. These machine learning inspired ansatzes have recently come to the fore in both a (first quantized) continuum and discrete Fock space representations, where however the inherent scaling of the latter approach for realistic interactions has so far limited practical applications. With application to the 'Gaussian Process State', a recently introduced ansatz inspired by systematically improvable kernel models in machine learning, we discuss different choices to define the representation of the computational Fock space. We show how local representations are particularly suited for stochastic sampling of expectation values, while also indicating a route to overcome the discrepancy in the scaling compared to continuum formulated models. We are able to show competitive accuracy for systems with up to 64 electrons, including a simplified (yet fully ab initio) model of the Mott transition in three-dimensional hydrogen, indicating a significant improvement over similar approaches, even for moderate numbers of configurational samples.

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。

↑