arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2302.02899cond-mat.str-elcond-mat.mtrl-sci
收藏

重整化密度矩阵降维:从从头算多体计算中学习涌现模型的严格框架

Renormalized density matrix downfolding: A rigorous framework in learning emergent models from ab initio many-body calculations

Yueqing Chang, Sonali Joshi, Lucas K. Wagner

AI总结:

提出重整化密度矩阵降维(RDMD)框架,从从头算数据中系统性地推导非微扰有效模型,并应用于氢链到自旋模型的降维,在强耦合区域实现定量准确的映射。

摘要:

我们提出了一个广义框架——重整化密度矩阵降维(RDMD),用于从从头算计算中推导出可系统性改进、高度准确且非微扰的有效模型。该框架超越了从头算计算作为计算所提出哈密顿量参数的常见作用。相反,RDMD提供了能力来决定给定的有效希尔伯特空间是否可以从从头算数据中识别,并评估拟设哈密顿量的相对质量。任何从头算求解方法都可以用作数据源,并且随着从头算解的改进,所得模型也随之改进。我们通过将氢链降维为自旋模型的应用来展示该框架,在其中我们找到了即使在标准方法失效的强耦合区域中也能进行非微扰映射的原子间距离,并计算了一个重整化自旋模型哈密顿量,该哈密顿量定量地再现了从头算动力学。

英文摘要:

We present a generalized framework, renormalized density matrix downfolding (RDMD), to derive systematically improvable, highly accurate, and nonperturbative effective models from ab initio calculations. This framework moves beyond the common role of ab initio calculations as calculating the parameters of a proposed Hamiltonian. Instead, RDMD provides the capability to decide whether a given effective Hilbert space can be identified from the ab initio data and assess the relative quality of ansatz Hamiltonians. Any method of ab initio solution can be used as a data source, and as the ab initio solutions improve, the resultant model also improves. We demonstrate the framework in an application to the downfolding of a hydrogen chain to a spin model, in which we find the interatomic separations for which a nonperturbative mapping can be made even in the strong coupling regime where standard methods fail, and compute a renormalized spin model Hamiltonian that quantitatively reproduces the ab initio dynamics.

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。

↑