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Modeling core-level excitations with variationally optimized reduced-density matrices and the extended random phase approximation

机译:模拟优化减小密度矩阵和扩展随机相位近似模拟核心级激励

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摘要

The information contained within ground-state one-and two-electron reduced-density matrices (RDMs) can be used to compute wave functions and energies for electronically excited states through the extended random phase approximation (ERPA). The ERPA is an appealing framework for describing excitations out of states obtained via the variational optimization of the two-electron RDM (2-RDM), as the variational 2-RDM (v2RDM) approach itself can only be used to describe the lowest-energy state of a given spin symmetry. The utility of the ERPA for predicting near-edge features relevant to x-ray absorption spectroscopy is assessed for the case that the 2-RDM is obtained from a ground-state v2RDM-driven complete active space self-consistent field (CASSCF) computation. A class of killer conditions for the CASSCF-specific ERPA excitation operator is derived, and it is demonstrated that a reliable description of core-level excitations requires an excitation operator that fulfills these conditions; the core-valence separation (CVS) scheme yields such an operator. Absolute excitation energies evaluated within the CASSCF/CVS-ERPA framework are slightly more accurate than those obtained from the usual random phase approximation (RPA), but the CVS-ERPA is not more accurate than RPA for predicting the relative positions of near-edge features. Nonetheless, CVS-ERPA is established as a reasonable starting point for the treatment of core-level excitations using variationally optimized 2-RDMs. Published by AIP Publishing.
机译:包含在地态的一个和两个电子减小密度矩阵(RDMS)中包含的信息可用于计算通过扩展随机相位近似(ERPA)来计算电子激发状态的波函数和能量。 ERPA是一种吸引人的框架,用于描述通过双电子RDM(2-RDM)的变分优化获得的状态的激发,因为变分2-RDM(V2RDM)方法本身只能用于描述最低能量给定自旋对称的状态。用于预测与X射线吸收光谱相关的近边缘特征的ERPA的效用被评估了2-RDM从地态V2RDM驱动的完整活动空间自我一致性字段(CASSCF)计算获得的情况。派生CASSCF特异性ERPA激励算子的一类杀手条件,并证明了核心水平激励的可靠描述需要满足这些条件的激励算子;核心价分离(CVS)方案产生这样的操作员。在Casscf / CVS-ERPA框架内评估的绝对励磁能量比从通常的随机相位近似(RPA)获得的那些略微准确,但CVS-ERPA不比RPA更准确,以预测近边节特征的相对位置。尽管如此,CVS-ERPA被建立为使用分分优化的2-RDMS治疗核心水平激发的合理起点。通过AIP发布发布。

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