CDS&E: D3SC: Understanding and Discovering Two-Dimensional Electrocatalyts Using Grand-Canonical Density Functional Theory (DFT) and Machine Learning

CDS

• 批准号: 1900039
• 负责人: Yuanyue Liu
• 金额: $ 42万
• 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: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1900039&HistoricalAwards=false
• 关键词: CDS&E; D3SC; Understanding; Discovering; Two

A major challenge in renewable energy is the inefficient interconversion of electrical and chemical energy. New electrocatalysts are needed to solve this challenge. Two-dimensional (2D) materials are only one or a few atoms thick and as such, they serve as desirable, high surface area electrocatalysts for energy conversions. In this project, Dr. Yuanyue Liu is using computer simulations to provide a fundamental understanding of the capabilities of 2D materials as electrocatalysts. His simulations use artificial intelligence to accelerate the discovery process. Dr. Liu is training students in computational chemistry, catalysis and energy conversions by actively engaging them in multidisciplinary research and integrating his research findings into the classroom. Dr. Liu is working with the Faculty Technology Studio to create educational videos that illustrate electrocatalysis applications. These videos are being used in various outreach programs to increase the interest of K-12 students and underrepresented groups in pursuing science, technology, engineering and mathematics (STEM) careers.With funding from the Chemical Catalysis Program of the Chemistry Division, Dr. Yuanyue Liu at the University of Texas at Austin is developing an improved understanding of the electrocatalytic mechanisms of 2D materials (an emerging class of electrocatalysts). By performing grand-canonical density functional theory (gc-DFT) calculations and coupling the calculations with machine learning, the team searches for new 2D electrocatalysts with better performance. Conventional DFT calculations often neglect the effects of varying charge and constant potential in electrochemical reactions, yet these parameters are found to be critical for 2D materials. The gc-DFT includes these effects and thus can elucidate the mechanisms of 2D electrocatalysts for reactions such as hydrogen evolution, oxygen reduction/evolution, and carbon dioxide reduction. Machine learning models are used to uncover the underlying factors that govern the catalytic activity and selectivity. The models also predict better catalysts from the large 2D materials library.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.

可再生能源的一个主要挑战是电能和化学能的低效相互转换。需要新的电催化剂来解决这一挑战。二维（2D）材料只有一个或几个原子厚，因此，它们可以作为理想的、高表面积的能量转换电催化剂。在这个项目中，刘元岳博士正在使用计算机模拟来提供对二维材料作为电催化剂的能力的基本理解。他的模拟使用人工智能来加速发现过程。刘博士通过积极参与多学科研究并将其研究成果融入课堂，培养学生在计算化学、催化和能量转换方面的能力。刘博士正在与学院技术工作室合作，制作说明电催化应用的教育视频。这些视频被用于各种推广项目，以提高K-12学生和代表性不足的群体对追求科学、技术、工程和数学（STEM）职业的兴趣。在化学学部化学催化项目的资助下，德克萨斯大学奥斯汀分校的yuyuue Liu博士正在对二维材料（一种新兴的电催化剂）的电催化机制进行进一步的了解。通过执行大规范密度泛函理论（gc-DFT）计算并将计算与机器学习相结合，该团队寻找具有更好性能的新型2D电催化剂。传统的DFT计算往往忽略了电化学反应中变电荷和恒电位的影响，然而这些参数对二维材料来说是至关重要的。gc-DFT包括这些效应，因此可以阐明二维电催化剂的反应机制，如析氢、氧还原/析氧和二氧化碳还原。机器学习模型用于揭示控制催化活性和选择性的潜在因素。该模型还预测了大型二维材料库中更好的催化剂。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

Origin of Selective Production of Hydrogen Peroxide by Electrochemical Oxygen Reduction

• DOI: 10.1021/jacs.1c02186
• 发表时间: 2021-06-16
• 期刊: JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
• 影响因子: 15
• 作者: Zhao, Xunhua;Liu, Yuanyue
• 通讯作者: Liu, Yuanyue

Methanol tolerance of atomically dispersed single metal site catalysts: mechanistic understanding and high-performance direct methanol fuel cells

• DOI: 10.1039/d0ee01968b
• 发表时间: 2020-10-01
• 期刊: ENERGY & ENVIRONMENTAL SCIENCE
• 影响因子: 32.5
• 作者: Shi, Qiurong;He, Yanghua;Wu, Gang
• 通讯作者: Wu, Gang

Redox-tunable Lewis bases for electrochemical carbon dioxide capture

• DOI: 10.1038/s41560-022-01137-z
• 发表时间: 2022-10-13
• 期刊: NATURE ENERGY
• 影响因子: 56.7
• 作者: Li, Xing;Zhao, Xunhua;Liu, Yayuan
• 通讯作者: Liu, Yayuan

Ultrafine Oxygen-Defective Iridium Oxide Nanoclusters for Efficient and Durable Water Oxidation at High Current Densities in Acidic Media

• DOI: 10.1039/d0ta07093a
• 发表时间: 2021-05
• 期刊: ECS Meeting Abstracts
• 作者: Lifeng Liu;Zhiyan Yu

Dynamic Stability of Copper Single-Atom Catalysts under Working Conditions

• DOI: 10.1021/jacs.2c07178
• 发表时间: 2022-09-11
• 期刊: JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
• 影响因子: 15
• 作者: Bai, Xiaowan;Zhao, Xunhua;Liu, Yuanyue
• 通讯作者: Liu, Yuanyue