优化相关激发态波函数的集合变异蒙特卡洛算法

IF 2.9 Q3 CHEMISTRY, PHYSICAL
William A Wheeler, Kevin G Kleiner, Lucas K Wagner
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引用次数: 0

摘要

变异蒙特卡洛方法最近被应用于激发态的计算;然而,什么目标函数最有效仍是一个悬而未决的问题。一种很有前途的方法是利用惩罚来优化激发态,以尽量减少与低特征态的重叠,但这种方法的缺点是必须一次计算一个态。我们推导出一个通用框架,用于构建在多体哈密顿最低 N 个特征状态处具有最小值的目标函数。目标函数使用能量的加权平均值和重叠惩罚,必须满足几个条件。我们证明了在罚金有限的情况下,该目标函数在精确特征点处具有最小值,并提供了几种最小化目标函数的策略。我们利用 ab initio 变分蒙特卡洛计算一氧化碳分子的退化第一激发态来演示该方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ensemble variational Monte Carlo for optimization of correlated excited state wave functions
Variational Monte Carlo methods have recently been applied to the calculation of excited states; however, it is still an open question what objective function is most effective. A promising approach is to optimize excited states using a penalty to minimize overlap with lower eigenstates, which has the drawback that states must be computed one at a time. We derive a general framework for constructing objective functions with minima at the the lowest N eigenstates of a many-body Hamiltonian. The objective function uses a weighted average of the energies and an overlap penalty, which must satisfy several conditions. We show this objective function has a minimum at the exact eigenstates for a finite penalty, and provide a few strategies to minimize the objective function. The method is demonstrated using ab initio variational Monte Carlo to calculate the degenerate first excited state of a CO molecule.
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来源期刊
CiteScore
3.70
自引率
11.50%
发文量
46
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