Minimization and Eulerian Formulation of Differential Geormetry Based Nonpolar Multiscale Solvation Models

Zhan Chen
{"title":"Minimization and Eulerian Formulation of Differential Geormetry Based Nonpolar Multiscale Solvation Models","authors":"Zhan Chen","doi":"10.1515/mlbmb-2016-0005","DOIUrl":null,"url":null,"abstract":"Abstract In this work, the existence of a global minimizer for the previous Lagrangian formulation of nonpolar solvation model proposed in [1] has been proved. One of the proofs involves a construction of a phase field model that converges to the Lagrangian formulation. Moreover, an Eulerian formulation of nonpolar solvation model is proposed and implemented under a similar parameterization scheme to that in [1]. By doing so, the connection, similarity and difference between the Eulerian formulation and its Lagrangian counterpart can be analyzed. It turns out that both of them have a great potential in solvation prediction for nonpolar molecules, while their decompositions of attractive and repulsive parts are different. That indicates a distinction between phase field models of solvation and our Eulerian formulation.","PeriodicalId":34018,"journal":{"name":"Computational and Mathematical Biophysics","volume":"4 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2016-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/mlbmb-2016-0005","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational and Mathematical Biophysics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1515/mlbmb-2016-0005","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Mathematics","Score":null,"Total":0}
引用次数: 3

Abstract

Abstract In this work, the existence of a global minimizer for the previous Lagrangian formulation of nonpolar solvation model proposed in [1] has been proved. One of the proofs involves a construction of a phase field model that converges to the Lagrangian formulation. Moreover, an Eulerian formulation of nonpolar solvation model is proposed and implemented under a similar parameterization scheme to that in [1]. By doing so, the connection, similarity and difference between the Eulerian formulation and its Lagrangian counterpart can be analyzed. It turns out that both of them have a great potential in solvation prediction for nonpolar molecules, while their decompositions of attractive and repulsive parts are different. That indicates a distinction between phase field models of solvation and our Eulerian formulation.
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
基于非极性多尺度溶剂化模型的微分几何最小化和欧拉公式
本文证明了[1]中提出的非极性溶剂化模型的拉格朗日公式的全局极小值的存在性。其中一个证明涉及一个相场模型的构造,该模型收敛于拉格朗日公式。此外,提出了非极性溶剂化模型的欧拉公式,并在与[1]类似的参数化方案下实现。这样,就可以分析欧拉公式与拉格朗日公式之间的联系、相同点和不同点。结果表明,这两种方法在非极性分子的溶剂化预测中都有很大的潜力,但它们的吸引部分和排斥部分的分解是不同的。这表明了溶剂化相场模型和欧拉公式之间的区别。
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
Computational and Mathematical Biophysics
Computational and Mathematical Biophysics Mathematics-Mathematical Physics
CiteScore
2.50
自引率
0.00%
发文量
8
审稿时长
30 weeks
期刊最新文献
Optimal control and bifurcation analysis of SEIHR model for COVID-19 with vaccination strategies and mask efficiency Assessing the impact of information-induced self-protection on Zika transmission: A mathematical modeling approach Optimal control of susceptible mature pest concerning disease-induced pest-natural enemy system with cost-effectiveness On building machine learning models for medical dataset with correlated features A mathematical study of the adrenocorticotropic hormone as a regulator of human gene expression in adrenal glands
×
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