Ensemble variational Monte Carlo for optimization of correlated excited state wave functions

IF 2.9 Q3 CHEMISTRY, PHYSICAL Electronic Structure Pub Date : 2024-04-08 DOI:10.1088/2516-1075/ad38f8
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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
优化相关激发态波函数的集合变异蒙特卡洛算法
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。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
3.70
自引率
11.50%
发文量
46
期刊最新文献
Improving the precision of work-function calculations within plane-wave density functional theory Self-similarity of quantum transport in graphene using electrostatic gate and substrate Facilities and practices for linear response Hubbard parameters U and J in Abinit Approaching periodic systems in ensemble density functional theory via finite one-dimensional models Regulating electronic structure of anionic oxygen by Ti4+ doping to stabilize layered Li-rich oxide cathodes for Li-ion batteries
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1