• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

相似文献

1
Deriving Force-Field Parameters from First Principles Using a Polarizable and Higher Order Dispersion Model.从第一性原理出发,使用极化和更高阶色散模型推导出力场参数。
J Chem Theory Comput. 2019 Mar 12;15(3):1875-1883. doi: 10.1021/acs.jctc.8b01105. Epub 2019 Feb 14.
2
Evaluating Force-Field London Dispersion Coefficients Using the Exchange-Hole Dipole Moment Model.利用交换空穴偶极矩模型评估力场伦敦色散系数。
J Chem Theory Comput. 2017 Dec 12;13(12):6146-6157. doi: 10.1021/acs.jctc.7b00522. Epub 2017 Nov 30.
3
Deriving a Polarizable Force Field for Biomolecular Building Blocks with Minimal Empirical Calibration.基于最小经验校准的生物分子构建基块的可极化力场的推导。
J Phys Chem B. 2020 Mar 5;124(9):1628-1636. doi: 10.1021/acs.jpcb.9b10903. Epub 2020 Feb 19.
4
Density-Dependent Formulation of Dispersion-Repulsion Interactions in Hybrid Multiscale Quantum/Molecular Mechanics (QM/MM) Models.混合多尺度量子/分子力学(QM/MM)模型中色散-排斥相互作用的密度相关公式。
J Chem Theory Comput. 2018 Mar 13;14(3):1671-1681. doi: 10.1021/acs.jctc.7b00912. Epub 2018 Feb 22.
5
Molecular dynamics simulations of a DMPC bilayer using nonadditive interaction models.使用非加和相互作用模型对二肉豆蔻酰磷脂酰胆碱双层进行分子动力学模拟。
Biophys J. 2009 Jan;96(2):385-402. doi: 10.1016/j.bpj.2008.09.048.
6
Evaluating the London Dispersion Coefficients of Protein Force Fields Using the Exchange-Hole Dipole Moment Model.使用交换空穴偶极矩模型评估蛋白质力场的伦敦色散系数。
J Phys Chem B. 2018 Jul 5;122(26):6690-6701. doi: 10.1021/acs.jpcb.8b02814. Epub 2018 Jun 22.
7
Charge-dependent model for many-body polarization, exchange, and dispersion interactions in hybrid quantum mechanical/molecular mechanical calculations.混合量子力学/分子力学计算中多体极化、交换和色散相互作用的电荷依赖模型。
J Chem Phys. 2007 Nov 21;127(19):194101. doi: 10.1063/1.2778428.
8
Development of a Polarizable Force Field for Molecular Dynamics Simulations of Lithium-Ion Battery Electrolytes: Sulfone-Based Solvents and Lithium Salts.用于锂离子电池电解液的分子动力学模拟的极化力场的开发:砜基溶剂和锂盐。
J Phys Chem B. 2021 Oct 14;125(40):11242-11255. doi: 10.1021/acs.jpcb.1c05744. Epub 2021 Sep 29.
9
Exploring Ion Polarizabilities and Their Correlation with van der Waals Radii: A Theoretical Investigation.探索离子极化率及其与范德华半径的相关性:一项理论研究。
J Chem Theory Comput. 2024 Oct 8;20(19):8505-8516. doi: 10.1021/acs.jctc.4c00632. Epub 2024 Sep 28.
10
Molecular Modeling of Water-in-Salt Electrolytes: A Comprehensive Analysis of Polarization Effects and Force Field Parameters in Molecular Dynamics Simulations.盐包水电解质的分子模拟:分子动力学模拟中极化效应和力场参数的综合分析
J Chem Theory Comput. 2023 Sep 12;19(17):5712-5730. doi: 10.1021/acs.jctc.3c00171. Epub 2023 Aug 1.

引用本文的文献

1
van der Waals Radii of Free and Bonded Atoms from Hydrogen (Z = 1) to Oganesson (Z = 118).从氢(Z = 1)到奥加涅森(Z = 118)的自由原子和键合原子的范德华半径
J Chem Theory Comput. 2024 Sep 10;20(17):7469-7478. doi: 10.1021/acs.jctc.4c00784. Epub 2024 Aug 29.
2
Regularized by Physics: Graph Neural Network Parametrized Potentials for the Description of Intermolecular Interactions.基于物理的正则化:用于描述分子间相互作用的图神经网络参数化势
J Chem Theory Comput. 2023 Jan 12;19(2):562-79. doi: 10.1021/acs.jctc.2c00661.
3
Perspective on the Current State-of-the-Art of Quantum Computing for Drug Discovery Applications.对药物发现应用中量子计算的最新进展的看法。
J Chem Theory Comput. 2022 Dec 13;18(12):7001-7023. doi: 10.1021/acs.jctc.2c00574. Epub 2022 Nov 10.
4
A collection of forcefield precursors for metal-organic frameworks.金属有机框架的力场前体集合。
RSC Adv. 2019 Nov 13;9(63):36492-36507. doi: 10.1039/c9ra07327b. eCollection 2019 Nov 11.
5
New scaling relations to compute atom-in-material polarizabilities and dispersion coefficients: part 1. Theory and accuracy.用于计算材料中原子极化率和色散系数的新标度关系:第1部分。理论与精度。
RSC Adv. 2019 Jun 19;9(34):19297-19324. doi: 10.1039/c9ra03003d.
6
Systematic optimization of a fragment-based force field against experimental pure-liquid properties considering large compound families: application to oxygen and nitrogen compounds.针对考虑大型化合物家族的实验纯液体性质,基于片段的力场的系统优化:应用于氧和氮化合物。
Phys Chem Chem Phys. 2021 Sep 7;23(33):17774-17793. doi: 10.1039/d1cp02001c. Epub 2021 Aug 5.
7
Deriving a Polarizable Force Field for Biomolecular Building Blocks with Minimal Empirical Calibration.基于最小经验校准的生物分子构建基块的可极化力场的推导。
J Phys Chem B. 2020 Mar 5;124(9):1628-1636. doi: 10.1021/acs.jpcb.9b10903. Epub 2020 Feb 19.
8
Data-Driven Mapping of Gas-Phase Quantum Calculations to General Force Field Lennard-Jones Parameters.基于数据的气相量子计算到通用力场 Lennard-Jones 参数的映射。
J Chem Theory Comput. 2020 Feb 11;16(2):1115-1127. doi: 10.1021/acs.jctc.9b00713. Epub 2020 Jan 17.
9
Development and Validation of the Quantum Mechanical Bespoke Protein Force Field.量子力学定制蛋白质力场的开发与验证。
ACS Omega. 2019 Aug 27;4(11):14537-14550. doi: 10.1021/acsomega.9b01769. eCollection 2019 Sep 10.
10
Atomic Partitioning of the MPn (n = 2, 3, 4) Dynamic Electron Correlation Energy by the Interacting Quantum Atoms Method: A Fast and Accurate Electrostatic Potential Integral Approach.用相互作用量子原子方法对MPn(n = 2, 3, 4)动态电子相关能进行原子划分:一种快速且精确的静电势积分方法
J Comput Chem. 2019 Dec 15;40(32):2793-2800. doi: 10.1002/jcc.26037. Epub 2019 Aug 2.

本文引用的文献

1
QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics.QUBEKit:从量子力学中自动推导出力场参数。
J Chem Inf Model. 2019 Apr 22;59(4):1366-1381. doi: 10.1021/acs.jcim.8b00767. Epub 2019 Feb 22.
2
Toward Learned Chemical Perception of Force Field Typing Rules.朝着学习化学感知力场类型规则的方向发展。
J Chem Theory Comput. 2019 Jan 8;15(1):402-423. doi: 10.1021/acs.jctc.8b00821. Epub 2018 Dec 24.
3
Escaping Atom Types in Force Fields Using Direct Chemical Perception.利用直接化学感知逃避力场中的原子类型。
J Chem Theory Comput. 2018 Nov 13;14(11):6076-6092. doi: 10.1021/acs.jctc.8b00640. Epub 2018 Oct 30.
4
Evaluating the London Dispersion Coefficients of Protein Force Fields Using the Exchange-Hole Dipole Moment Model.使用交换空穴偶极矩模型评估蛋白质力场的伦敦色散系数。
J Phys Chem B. 2018 Jul 5;122(26):6690-6701. doi: 10.1021/acs.jpcb.8b02814. Epub 2018 Jun 22.
5
Developing a molecular dynamics force field for both folded and disordered protein states.为折叠和无序的蛋白质状态开发分子动力学力场。
Proc Natl Acad Sci U S A. 2018 May 22;115(21):E4758-E4766. doi: 10.1073/pnas.1800690115. Epub 2018 May 7.
6
Optimized Lennard-Jones Parameters for Druglike Small Molecules.优化适用于类药性小分子的 Lennard-Jones 参数。
J Chem Theory Comput. 2018 Jun 12;14(6):3121-3131. doi: 10.1021/acs.jctc.8b00172. Epub 2018 May 7.
7
Fixed-Charge Atomistic Force Fields for Molecular Dynamics Simulations in the Condensed Phase: An Overview.固定电荷原子力场在凝聚相分子动力学模拟中的应用综述。
J Chem Inf Model. 2018 Mar 26;58(3):565-578. doi: 10.1021/acs.jcim.8b00042. Epub 2018 Mar 13.
8
Machine Learning of Partial Charges Derived from High-Quality Quantum-Mechanical Calculations.机器学习部分电荷源于高质量量子力学计算。
J Chem Inf Model. 2018 Mar 26;58(3):579-590. doi: 10.1021/acs.jcim.7b00663. Epub 2018 Mar 7.
9
Harmonic Force Constants for Molecular Mechanics Force Fields via Hessian Matrix Projection.通过海森矩阵投影得到分子力学力场的谐力常数
J Chem Theory Comput. 2018 Jan 9;14(1):274-281. doi: 10.1021/acs.jctc.7b00785. Epub 2017 Dec 6.
10
Evaluating Force-Field London Dispersion Coefficients Using the Exchange-Hole Dipole Moment Model.利用交换空穴偶极矩模型评估力场伦敦色散系数。
J Chem Theory Comput. 2017 Dec 12;13(12):6146-6157. doi: 10.1021/acs.jctc.7b00522. Epub 2017 Nov 30.

从第一性原理出发,使用极化和更高阶色散模型推导出力场参数。

Deriving Force-Field Parameters from First Principles Using a Polarizable and Higher Order Dispersion Model.

机构信息

AIMMS Division of Molecular Toxicology, Department of Chemistry and Pharmaceutical Sciences, Faculty of Science , Vrije Universiteit Amsterdam , De Boelelaan 1108 , 1081 HZ Amsterdam , the Netherlands.

出版信息

J Chem Theory Comput. 2019 Mar 12;15(3):1875-1883. doi: 10.1021/acs.jctc.8b01105. Epub 2019 Feb 14.

DOI:10.1021/acs.jctc.8b01105
PMID:30763086
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6581419/
Abstract

In this work we propose a strategy based on quantum mechanical (QM) calculations to parametrize a polarizable force field for use in molecular dynamics (MD) simulations. We investigate the use of multiple atoms-in-molecules (AIM) strategies to partition QM determined molecular electron densities into atomic subregions. The partitioned atomic densities are subsequently used to compute atomic dispersion coefficients from effective exchange-hole-dipole moment (XDM) calculations. In order to derive values for the repulsive van der Waals parameters from first principles, we use a simple volume relation to scale effective atomic radii. Explicit inclusion of higher order dispersion coefficients was tested for a series of alkanes, and we show that combining C and C attractive terms together with a C repulsive potential yields satisfying models when used in combination with our van der Waals parameters and electrostatic and bonded parameters as directly obtained from quantum calculations as well. This result highlights that explicit inclusion of higher order dispersion terms could be viable in simulation, and it suggests that currently available QM analysis methods allow for first-principles parametrization of molecular mechanics models.

摘要

在这项工作中,我们提出了一种基于量子力学(QM)计算的策略,用于参数化极化力场,以用于分子动力学(MD)模拟。我们研究了使用多种原子在分子(AIM)策略将 QM 确定的分子电子密度分配到原子子区域。然后,使用有效交换孔偶极矩(XDM)计算来计算分区原子密度的原子色散系数。为了从第一性原理推导出排斥范德华参数的值,我们使用简单的体积关系来缩放有效原子半径。我们对一系列烷烃进行了高阶色散系数的显式包含测试,并表明当与我们的范德华参数以及静电和键合参数结合使用时,将 C 和 C 吸引项组合在一起,并使用 C 排斥势,可以得到令人满意的模型,这些参数直接从量子计算中获得。这一结果表明,在模拟中显式包含高阶色散项是可行的,并且表明目前可用的 QM 分析方法允许对分子力学模型进行第一性原理参数化。