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Data-Driven Many-Body Models for Molecular Simulations of Ions in Water: From Ionic Clusters to Concentrated Electrolyte Solutions

Data-Driven Many-Body Models for Molecular Simulations of Ions in Water: From Ionic Clusters to Concentrated Electrolyte Solutions
用于水中离子分子模拟的数据驱动多体模型:从离子簇到浓缩电解质溶液
批准号:
1954895
负责人:
Francesco Paesani
金额:
$49.06万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-04-01 至 2024-03-31

项目摘要

项目成果

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中文摘要
翻译
加州大学圣地亚哥分校的弗朗西斯科·帕萨尼博士得到了化学系化学理论、模型和计算方法项目的支持。定量、分子水平地表征水环境中离子在不同条件下的结构、热力学、动力学和光谱性质,是理解几个基本过程的关键,这些过程包括大气中的离子-分子反应和离子诱导成核、溶液中的酸碱反应、生物通道中的离子传输以及气/液和固/液界面的多相过程。要对水合离子在不同阶段和不同环境中的行为进行统一的描述尤其具有挑战性,这需要对与离子-离子、离子-水和水-水相互作用相关的竞争焓和熵效应进行定量评估,这些相互作用进一步受到核量子效应的调节。通过将完全来自高级从头计算参考数据的数据驱动的多体势能函数与经典和量子分子动力学相结合,帕萨尼将把他的多体分子动力学(MB-MD)方法扩展到水中离子的建模,从气相团簇到整体溶液。MB-MD方法为水体系的分子模拟提供了一个准确而有效的理论/计算框架,具有很高的准确性和预测能力。在这个研究项目中,Pesaani的目标是从微观上理解从小的气相团簇到大块溶液的单离子水化结构和热力学,根据单个多体贡献来确定单离子水化自由能和离子-离子缔合自由能,并根据基本的离子-水和水-水多体效应来分解它们,并表征电解液中水的氢键结构和动力学作为离子浓度和组成的函数,解缠离子-离子,离子-水和水-水贡献的函数。作为拟议研究的一部分,开发的理论/计算方法将对从化学到材料科学和生物学的广泛用户群体产生广泛影响,并将涉及对高中、本科生和研究生的培训和教育,他们将在理论、计算和物理化学方面获得坚实的基础。拟议项目的跨学科性质将为学生提供在不同学科之间工作的机会,这将使他们为广泛的职业生涯做好准备。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
Dr. Francesco Paesani of the University of California-San Diego is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry. A quantitative, molecular-level characterization of the structural, thermodynamic, dynamical, and spectroscopic properties of ions in aqueous environments under different conditions is key to understanding several fundamental processes, including ion-molecule reactions and ion-induced nucleation in the atmosphere, acid-base reactions in solution, ion transport in biological channels, and multiphase processes at air/liquid and solid/liquid interfaces. A unified picture of the behavior of hydrated ions across different phases and in different environments is particularly challenging, requiring a quantitative assessment of competing enthalpic and entropic effects associated with the interplay between ion–ion, ion–water, and water–water interactions, which are further modulated by nuclear quantum effects. By combining data-driven many-body potential energy functions derived entirely from high-level ab initio reference data with both classical and quantum molecular dynamics, Paesani will extend his many-body molecular dynamics (MB-MD) methodology to the modeling of ions in water, from gas-phase clusters to bulk solutions. The MB-MD methodology provides an accurate, yet efficient, theoretical/computational framework for molecular simulations of aqueous systems with high accuracy and predictive power. In this research project Paesani aims to develop a microscopic understanding of single-ion hydration structure and thermodynamics from small gas-phase clusters to bulk solutions in terms of individual many-body contributions, determine single-ion hydration free energies and ion–ion association free energies and decompose them in terms of elementary ion–water and water–water many-body effects, and characterize water hydrogen-bond structure and dynamics in electrolyte solutions as a function of ion concentration and composition, disentangling ion–ion, ion–water and water–water contributions. The theoretical/ computational methodology developed as part of the proposed research will have broad impact on a wide range of user communities from chemistry to materials science and biology, and will involve the training and education of high school, undergraduate and graduate students who will acquire a solid foundation in theoretical, computational, and physical chemistry. The interdisciplinary nature of the proposed project will provide the students with the opportunity to work at the interface of different disciplines which will prepare them for a wide range of careers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
期刊论文(4)
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科研奖励(0)
会议论文
DOI: 10.1063/5.0129613
发表时间: 2023
期刊: Chemical Physics Reviews
影响因子: --
作者: [Palos, Etienne, Dasgupta, Saswata, Lambros, Eleftherios, Paesani, Francesco]
通讯作者: Paesani, Francesco
Accurate Modeling of Bromide and Iodide Hydration with Data-Driven Many-Body Potentials
利用数据驱动的多体势对溴化物和碘化物水合进行精确建模
DOI: 10.1021/acs.jpcb.2c04698
发表时间: 2022
期刊: The Journal of Physical Chemistry B
影响因子: --
作者: [Caruso, Alessandro, Zhu, Xuanyu, Fulton, John L., Paesani, Francesco]
通讯作者: Paesani, Francesco
Hydration Structure of Na + and K + Ions in Solution Predicted by Data-Driven Many-Body Potentials
数据驱动的多体势预测溶液中 Na 和 K 离子的水合结构
DOI: 10.1021/acs.jpcb.2c05674
发表时间: 2022
期刊: The Journal of Physical Chemistry B
影响因子: --
作者: [Zhuang, Debbie, Riera, Marc, Zhou, Ruihan, Deary, Alexander, Paesani, Francesco]
通讯作者: Paesani, Francesco
Data-driven many-body models enable a quantitative description of chloride hydration from clusters to bulk
数据驱动的多体模型能够定量描述从簇到块状的氯化物水合
DOI: 10.1063/5.0059445
发表时间: 2021
期刊: The Journal of Chemical Physics
影响因子: --
作者: [Caruso, Alessandro, Paesani, Francesco]
通讯作者: Paesani, Francesco
Collaborative Research: CyberTraining: Implementation: Medium: Training Users, Developers, and Instructors at the Chemistry/Physics/Materials Science Interface
  • 批准号:
    2321104
  • 项目类别:
    Standard Grant
  • 资助金额:
    $33.33万
  • 财政年份:
    2024
  • 负责人:
    Francesco Paesani
  • 依托单位:
Frameworks: Data-Driven Software Infrastructure for Next-Generation Molecular Simulations
  • 批准号:
    2311260
  • 项目类别:
    Standard Grant
  • 资助金额:
    $292.81万
  • 财政年份:
    2023
  • 负责人:
    Francesco Paesani
  • 依托单位:
Disentangling Many-Body Effects and Coupling in the Vibrational Spectra of Aqueous Clusters
  • 批准号:
    2102309
  • 项目类别:
    Standard Grant
  • 资助金额:
    $78.94万
  • 财政年份:
    2021
  • 负责人:
    Francesco Paesani
  • 依托单位:
Molecular Characterization of Water Oxidation in Metal-Organic Frameworks through Computer Simulations
  • 批准号:
    1704063
  • 项目类别:
    Standard Grant
  • 资助金额:
    $44.35万
  • 财政年份:
    2018
  • 负责人:
    Francesco Paesani
  • 依托单位:
国内基金
海外基金
Data-driven Recommendation System Construction of an Online Medical Platform Based on the Fusion of Information