Hydration Structures and Perturbed Hydrogen Bond Network in Salt Solutions by Advanced Ab Initio Molecular Dynamics and Electronic Structure Simulation Methods

通过先进的从头算分子动力学和电子结构模拟方法研究盐溶液中的水合结构和扰动氢键网络

• 批准号: 2053195
• 负责人: Xifan Wu
• 金额: $ 36万
• 依托单位: Temple University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2053195&HistoricalAwards=false
• 关键词: Hydration; Structures; Perturbed; Hydrogen; Bond

NONTECHNICAL SUMMARYThis award supports research and educational activities with an aim to advance our fundamental understanding of the physical and chemical properties of salt water. Salt water is ubiquitous on earth, and it plays important roles in many physiological and biological processes, such as signal transduction in living cells as well as protein solubility and folding. The precise picture of how water molecules arrange themselves and move around salt ions is prerequisite to understanding the properties of salt water. This requires a quantum mechanical modeling for both the electrons and nuclei of water and salt molecules. However, due to the high computational cost, current computational studies can only model salt water in small simulation boxes for short time periods, which makes it difficult to extract accurate information about the long-range effects of salt molecules on the molecular and electronic structures of water. This project seeks to build theoretical tools and a computational framework to solve this problem by using machine learning methods. Utilizing computationally intensive calculations for only a few configurations of salt water, the PI and his team will build efficient deep learning models that can model salt water in simulation boxes containing thousands of water molecules for long time scales. The resulting deep learning models will not only be computationally efficient, but they will also be able to yield accurate predictions with comparable accuracy to advanced, fully quantum mechanical calculations. Both the microscopic and macroscopic properties of salt water will be accurately predicted by this approach and compared with experimental data.The participating postdocs and graduate students will carry out cutting-edge, interdisciplinary research based on advanced molecular dynamics simulations and machine learning techniques in the fields of condensed matter theory and quantum chemistry. Accurate deep learning models of salt solutions will be developed and distributed to the computational materials community. An outreach activity that explores the science of water will be developed and presented to high school students in New Jersey.TECHNICAL SUMMARYThis award supports research and educational activities that are aimed at understanding the hydration structure of important salt aqueous solutions and exploring how the hydrated ions will affect the hydrogen-bond network of liquid water. Ab initio molecular dynamics simulations based on density functional theory provide an ideal framework to study salt water from first principles. However, accurate predictions of salt water properties require a high-level functional approximation, and nuclear quantum effect should be taken into account due to the small mass of the proton. Accordingly, accurate first-principles calculations of salt water are computationally very expensive and cannot be applied to study the long-range effect of hydrated ions on the water structure. This PI and his team will overcome these computational challenges by combined deep learning techniques and advanced density functional theory calculations. Efficient deep molecular dynamics models of salt solutions will be built on Feynman path integral ab initio molecular dynamics simulations based on the hybrid meta-GGA SCAN0 density functional. Using deep learning models, molecular dynamics simulations of salt water can be performed in a simulation box containing thousands of water molecules at the nanosecond time scale, with a level of accuracy comparable to direct density functional theory calculations. With this powerful approach, the converged thermodynamic properties such as diffusivities and molecular structure in salt water can be accurately predicted and studied. Using the molecular trajectories of salt water, the electronic properties will also be modeled by deep learning techniques and compared to available experimental results.The participating postdocs and graduate students will carry out cutting-edge, interdisciplinary research based on advanced ab initio molecular dynamics simulations and machine learning techniques in the fields of condensed matter theory and quantum chemistry. Accurate deep learning models of salt solutions will be developed and distributed to the computational materials community. An outreach activity that explores the science of water will be developed and presented to high school students in New Jersey.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.

非技术总结该奖项支持研究和教育活动，旨在促进我们对咸水的物理和化学性质的基本了解。咸水在地球上普遍存在，在许多生理和生物过程中发挥着重要作用，如活细胞中的信号转导以及蛋白质的溶解和折叠。水分子如何在盐离子周围排列和运动的精确图像是了解盐水性质的先决条件。这需要对水和盐分子的电子和原子核进行量子力学建模。然而，由于计算成本较高，目前的计算研究只能在小的模拟箱中对盐水进行短时间的模拟，这使得很难提取关于盐分子对水的分子结构和电子结构的远程影响的准确信息。这个项目寻求建立理论工具和计算框架，通过使用机器学习方法来解决这个问题。PI和他的团队只利用对几种盐水配置的计算密集型计算，将建立有效的深度学习模型，可以在包含数千个水分子的模拟箱中对盐水进行长时间尺度的建模。由此产生的深度学习模型不仅在计算上高效，而且它们还能够产生与先进的、完全量子力学计算相媲美的准确预测。该方法将准确预测盐水的微观和宏观性质，并与实验数据进行比较。参赛博士后和研究生将基于先进的分子动力学模拟和机器学习技术，在凝聚态理论和量子化学领域开展前沿、跨学科的研究。将开发准确的SALT解决方案深度学习模型，并将其分发给计算材料社区。将开展一项探索水科学的推广活动，并向新泽西州的高中生介绍该奖项。该奖项支持旨在了解重要盐类水溶液的水化结构并探索水合离子如何影响液态水的氢键网络的研究和教育活动。基于密度泛函理论的从头算分子动力学模拟为从第一性原理研究盐水提供了一个理想的框架。然而，准确预测盐水的性质需要高水平的泛函近似，并且由于质子的小质量，应该考虑核量子效应。因此，精确的盐水第一性原理计算在计算上是非常昂贵的，不能用于研究水合离子对水结构的长期影响。这位PI和他的团队将通过结合深度学习技术和先进的密度泛函理论计算来克服这些计算挑战。基于混合Meta-GGA SCAN0密度泛函的Feynman路径积分从头算分子动力学模拟将建立有效的盐溶液深分子动力学模型。使用深度学习模型，盐水的分子动力学模拟可以在纳秒时间尺度上在包含数千个水分子的模拟箱中进行，其精度水平可与直接密度泛函理论计算相媲美。利用这种强大的方法，可以准确地预测和研究盐水中的扩散系数和分子结构等收敛的热力学性质。利用盐水的分子轨迹，电子性质也将通过深度学习技术进行建模，并与现有的实验结果进行比较。参与研究的博士后和研究生将基于凝聚态理论和量子化学领域的先进从头计算分子动力学模拟和机器学习技术，开展前沿的跨学科研究。将开发准确的SALT解决方案深度学习模型，并将其分发给计算材料社区。将开展一项探索水科学的外展活动，并将其颁发给新泽西州的高中生。这一奖项反映了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。

项目成果

DeePKS + ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials

DeePKS ABACUS 作为昂贵的量子力学模型和机器学习潜力之间的桥梁

• DOI: 10.1021/acs.jpca.2c05000
• 发表时间: 2022
• 期刊: The Journal of Physical Chemistry A
• 作者: Wenfei Li;Qi Ou;Yixiao Chen;Yu Cao;Renxi Liu;Chunyi Zhang;Daye Zheng;Chun Cai;Xifan Wu;Han Wang;Mohan Chen;Linfeng Zhang
• 通讯作者: Linfeng Zhang

Convert Widespread Paraelectric Perovskite to Ferroelectrics

将广泛使用的顺电钙钛矿转化为铁电材料

• DOI: 10.1103/physrevlett.128.197601
• 发表时间: 2022
• 期刊: Physical Review Letters
• 影响因子: 8.6
• 作者: Wang, Hongwei;Tang, Fujie;Stengel, Massimiliano;Xiang, Hongjun;An, Qi;Low, Tony;Wu, Xifan
• 通讯作者: Wu, Xifan

Exploring the impact of ions on oxygen K-edge X-ray absorption spectroscopy in NaCl solution using the GW-Bethe-Salpeter-equation approach

使用 GW-Bethe-Salpeter 方程方法探索 NaCl 溶液中离子对氧 K 边 X 射线吸收光谱的影响

• DOI: 10.1063/5.0167999
• 发表时间: 2023
• 期刊: The Journal of Chemical Physics
• 作者: Tang, Fujie;Shi, Kefeng;Wu, Xifan
• 通讯作者: Wu, Xifan

Structural and dynamic properties of solvated hydroxide and hydronium ions in water from ab initio modeling

从头开始建模水中溶剂化氢氧根和水合氢离子的结构和动态特性

• DOI: 10.1063/5.0094944
• 发表时间: 2022
• 期刊: The Journal of Chemical Physics
• 作者: Liu, Renxi;Zhang, Chunyi;Liang, Xinyuan;Liu, Jianchuan;Wu, Xifan;Chen, Mohan
• 通讯作者: Chen, Mohan