William A Wheeler, Kevin G Kleiner, Lucas K Wagner
{"title":"Ensemble variational Monte Carlo for optimization of correlated excited state wave functions","authors":"William A Wheeler, Kevin G Kleiner, Lucas K Wagner","doi":"10.1088/2516-1075/ad38f8","DOIUrl":null,"url":null,"abstract":"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 <italic toggle=\"yes\">N</italic> 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 <italic toggle=\"yes\">ab initio</italic> variational Monte Carlo to calculate the degenerate first excited state of a CO molecule.","PeriodicalId":42419,"journal":{"name":"Electronic Structure","volume":null,"pages":null},"PeriodicalIF":2.9000,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electronic Structure","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1088/2516-1075/ad38f8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0
Abstract
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.
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。