Computational Methods for Modeling Reaction Dynamics in Batteries and Catalysts

电池和催化剂反应动力学建模的计算方法

• 批准号: 2102317
• 负责人: Graeme Henkelman
• 金额: $ 45万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2102317&HistoricalAwards=false
• 关键词: Computational; Methods; Modeling; Reaction; Dynamics

Graeme Henkelman of the University of Texas at Austin is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to develop computational methods to understand the function of materials employed for the production and consumption of energy. This research is at the atomic scale and such a fundamental understanding will not only allow us to determine limitations of existing materials, but also consider new materials that have the potential to make energy production and usage more efficient. The second part of this work will incorporate tools from computer science, including machine learning, to improve the efficiency of our computational methods and accelerate the design of new materials. This project is expected to have a positive impact on the scientific community by providing these new computational tools as well as contribute to the growth and learning of the graduate and undergraduate students who will be developing these tools.In this project, Graeme Henkelman and his research group are setting out to develop computational methods to model the reaction dynamics in materials related to energy applications, including batteries and catalysts. More specifically, they seek to improve the efficiency of methods based upon transition state theory so that dynamics over experimental time scales can be modeled using forces and energy from density functional theory (DFT). In order to mitigate the computational expense of DFT a number of strategies will be followed to make the calculations as efficient as possible. First, a public kinetic database will be established with geometric information of reaction mechanisms, which can be used to propose transition states from a query structure to the database. Second, machine learning (ML) methods will be used to accelerate our calculations. Instead of fitting global potential energy surfaces, ML methods will be used to fit local energies, forces, and curvature to accelerate DFT calculations rather than replace them. Specifically, the ML models will: (1) provide a pre-conditioner for the optimization of minima and saddle points; (2) suggest local minima to facilitate the search for stable materials; (3) efficiently construct a hyperdynamics bias potential for modeling of rare events in energy materials directly with DFT. As well as using ML methods, this project will investigate how the choice of ML method, including neural networks and Gaussian processes, as well as the numerous hyperparameters, determine the shape and quality of the potential energy surface. Finally, methodology to take trajectories of catalytic reactions will be developed to build reaction networks, from which the overall activity of a catalyst or battery material can be understood. A broader impact of this research to the scientific community will be in the form of software that is freely distributed. In addition, a team of undergraduate students will be a part of this research and to use these tools.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.

德克萨斯大学奥斯汀分校的Graeme Henkelman获得了化学系化学理论、模型和计算方法项目的奖励，他开发了计算方法来理解用于生产和消耗能源的材料的功能。这项研究是在原子尺度上进行的，这样一个基本的理解不仅可以让我们确定现有材料的局限性，而且还可以考虑有可能使能源生产和使用更有效的新材料。这项工作的第二部分将结合计算机科学的工具，包括机器学习，以提高我们的计算方法的效率，加速新材料的设计。通过提供这些新的计算工具，该项目预计将对科学界产生积极影响，并有助于开发这些工具的研究生和本科生的成长和学习。在这个项目中，Graeme Henkelman和他的研究小组正着手开发计算方法来模拟与能源应用相关的材料的反应动力学，包括电池和催化剂。更具体地说，他们试图提高基于过渡态理论的方法的效率，这样实验时间尺度上的动力学可以使用密度泛函理论（DFT）的力和能量来建模。为了减少DFT的计算费用，将遵循一些策略以使计算尽可能高效。首先，利用反应机理的几何信息建立公共动力学数据库，利用该数据库提出从查询结构到数据库的过渡状态。其次，机器学习（ML）方法将用于加速我们的计算。ML方法将用于拟合局部能量、力和曲率，而不是拟合全局势能曲面，以加速DFT计算，而不是取代它们。具体而言，ML模型将：(1)为极小点和鞍点的优化提供预调节器；(2)建议局部最小值，以便寻找稳定的物质；(3)利用离散傅立叶变换有效地构建了能量材料中稀有事件直接建模的超动力学偏置势。除了使用机器学习方法外，该项目还将研究机器学习方法的选择，包括神经网络和高斯过程，以及众多超参数，如何决定势能面的形状和质量。最后，将开发采用催化反应轨迹的方法来构建反应网络，从中可以了解催化剂或电池材料的整体活性。这项研究对科学界的更广泛影响将以自由分发软件的形式出现。此外，一组本科生将参与这项研究并使用这些工具。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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

A Fast, Low-Cost and Simple Method for Predicting Atomic/Inter-Atomic Properties by Combining a Low Dimensional Deep Learning Model with a Fragment Based Graph Convolutional Network

• DOI: 10.3390/cryst12121740
• 发表时间: 2022-12
• 期刊: Crystals
• 影响因子: 2.7
• 作者: Peng Gao;Zonghang Liu;Jie Zhang;Jia-ao Wang;G. Henkelman

Atom-centered machine-learning force field package

• DOI: 10.1016/j.cpc.2023.108883
• 发表时间: 2023-08
• 期刊: Comput. Phys. Commun.
• 作者: Lei Li;Ryan A. Ciufo;Jiyoung Lee;Chuan Zhou-;Bo Lin;Jaeyoung Cho;Naman Katyal;G. Henkelman

Atomistic Mechanisms of Binary Alloy Surface Segregation from Nanoseconds to Seconds Using Accelerated Dynamics

使用加速动力学从纳秒到秒的二元合金表面偏析的原子机制

• DOI: 10.1021/acs.jctc.2c00303
• 发表时间: 2022
• 期刊: Journal of Chemical Theory and Computation
• 影响因子: 5.5
• 作者: Garza, Richard B.;Lee, Jiyoung;Nguyen, Mai H.;Garmon, Andrew;Perez, Danny;Li, Meng;Yang, Judith C.;Henkelman, Graeme;Saidi, Wissam A.
• 通讯作者: Saidi, Wissam A.