Combined Machine Learning and Computational Chemistry Guided Discovery of Chevrel Phases for Electrocatalytic CO2 Reduction

机器学习和计算化学相结合引导发现 Chevrel 相用于电催化 CO2 还原

• 批准号: 2016225
• 负责人: Charles Musgrave
• 金额: $ 37.54万
• 依托单位: University of Colorado at Boulder
• 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
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2016225&HistoricalAwards=false
• 关键词: Combined; Machine; Learning; Computational; Chemistry

Emission of carbon dioxide into the atmosphere is the major driver of climate change, and the path to a sustainable future will rely heavily on removing carbon dioxide from the air and either storing it or converting it into fuels or other valuable chemicals. One promising route for accomplishing this is to use electricity to drive the reaction of carbon dioxide with water to produce new chemicals. This reaction can occur on the surfaces of various materials with appropriate catalytic properties. Recently, an interesting family of materials known as Chevrels were shown to convert carbon dioxide to fuels. However, despite these promising initial results, this family of materials remains relatively unstudied and the efficiency of this reaction still needs substantial improvement to become economical. The objective of this work is to identify new Chevrel materials of the vast number of possible Chevrels that are capable of effectively converting carbon dioxide into valuable products. Identification of superior materials for this reaction could provide a major step towards reducing the level of carbon dioxide in the atmosphere and transitioning towards a sustainable future.Electrocatalytic production of methanol and C1+ products (reduction products with 1 carbon atom) remains a significant materials discovery challenge due to the poor selectivity and/or high overpotentials of existing electrochemical CO2 reduction (eCO2R) catalysts. Intercalated Chevrels (MyMo6X8, M = metal, X = S, Se, Te) are a promising but relatively unexplored class of materials that, like perovskites, provide a highly tunable framework for materials design and discovery with a broad range of potential applications. Furthermore, they were recently demonstrated to produce methanol selectively from CO2, suggesting that intercalated Chevrel phase materials may also be a relatively unexplored class of promising electrocatalysts that can be tuned for catalytic performance. The objective of this project is to computationally analyze and guide the design and accelerated discovery of new Chevrel phase electrocatalysts for efficient and selective CO2 conversions to valuable products. The strategy for accomplishing this goal is to 1) use state-of-the art computational quantum modeling tools to determine the mechanism of eCO2R on Chevrel surfaces in solvent and under an applied bias and 2) develop machine learned descriptors of catalyst stability, selectivity, and activity that enable the rational, high-throughput discovery of new high-performance Chevrel electrocatalysts that employ earth-abundant elements for economically-competitive CO2 conversions to valuable products. This research aligns closely with the topic areas of interest to this program, including renewable energy related catalysis, electrocatalysis, closing the carbon cycle, conversion of CO2, new catalyst designs and materials, basic understanding of catalyst materials and mechanisms and advances in tools for computational catalysis.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.

将二氧化碳排放到大气中是气候变化的主要驱动力，通往可持续的未来的道路将严重依赖于从空气中除去二氧化碳，然后将其存储或将其转化为燃料或其他有价值的化学物质。实现此目的的一种有希望的途径是利用电力驱动二氧化碳与水的反应，以产生新的化学物质。该反应可能发生在具有适当催化特性的各种材料的表面上。最近，一个有趣的材料家族称为雪佛兰，将二氧化碳转换为燃料。然而，尽管有这些有希望的初始结果，但这种材料系列仍然相对未被研究，并且该反应的效率仍然需要大量改进才能变得经济。这项工作的目的是确定能够有效将二氧化碳转化为有价值的产品的大量雪佛兰的新雪佛兰材料。鉴定该反应的优质材料可以为降低大气中二氧化碳的水平，并过渡到可持续的未来。甲醇和C1+产物的电催化产生（用1个碳原子还原产品）仍然​​是由于选择性不佳的挑战和/或/或/或高度超过现有的cotrapers cotraucty（ECO）caTREC2 REDRACT（ECO），这仍然是一个重要的材料发现挑战。插入的雪佛兰（MyMo6x8，M =金属，X = S，SE，TE）是一种有前途但相对未探索的材料类，如钙钛矿，为具有广泛潜在应用的材料设计和发现提供了高度可调的框架。此外，最近证明它们可以从CO2中选择性地产生甲醇，这表明嵌入的雪佛兰相材料也可能是一类相对未开发的有希望的电催化剂类，可以调整以催化性能。该项目的目的是计算分析和指导新的雪佛兰相电催化剂的设计和加速发现，以高效且选择性的二氧化碳转换为有价值的产品。实现这一目标的策略是1）使用最先进的计算量子建模工具来确定溶剂和施加偏见的雪佛兰表面上ECO2R的机制； 2）开发机器的催化剂稳定性，选择性和活动的机器描述符，以实现新的高度元素chevrel Element Element Element Electorcatsimation Electocats chevrel Electrocats的发现经济竞争性的二氧化碳转化为有价值的产品。这项研究与该计划的主题领域紧密相吻合，包括可再生能源相关的催化，电催化，关闭碳周期，转换二氧化碳的转换，新的催化剂设计和材料，对催化剂材料的基本理解和基本理解，以及在计算催化的工具上的基本理解，这些奖项反映了NSF的构建范围和构建范围的依据，这反映了NSF的构建范围。审查标准。

项目成果

Revised Nitrogen Reduction Scaling Relations from Potential-Dependent Modeling of Chemical and Electrochemical Steps

根据化学和电化学步骤的电位相关建模修正氮还原比例关系

• DOI: 10.1021/acscatal.3c01978
• 发表时间: 2023
• 期刊: ACS Catalysis
• 影响因子: 12.9
• 作者: Tezak, Cooper R.;Singstock, Nicholas R.;Alherz, Abdulaziz W.;Vigil-Fowler, Derek;Sutton, Christopher A.;Sundararaman, Ravishankar;Musgrave, Charles B.
• 通讯作者: Musgrave, Charles B.

A Computational Framework to Accelerate the Discovery of Perovskites for Solar Thermochemical Hydrogen Production: Identification of Gd Perovskite Oxide Redox Mediators

• DOI: 10.1002/adfm.202200201
• 发表时间: 2022-03-20
• 期刊: ADVANCED FUNCTIONAL MATERIALS
• 影响因子: 19
• 作者: Bare, Zachary-L;Morelock, Ryan N.;Musgrave, Charles B.
• 通讯作者: Musgrave, Charles B.

Bond-Valence Parameterization for the Accurate Description of DFT Energetics

用于准确描述 DFT 能量学的键价参数化

• DOI: 10.1021/acs.jctc.1c01113
• 发表时间: 2022
• 期刊: Journal of Chemical Theory and Computation
• 影响因子: 5.5
• 作者: Morelock, Ryan J.;Bare, Zachary J.;Musgrave, Charles B.
• 通讯作者: Musgrave, Charles B.

Machine Learning Guided Synthesis of Multinary Chevrel Phase Chalcogenides

机器学习引导多元 Chevrel 相硫属化物的合成

• DOI: 10.1021/jacs.1c02971
• 发表时间: 2021
• 期刊: Journal of the American Chemical Society
• 影响因子: 15
• 作者: Singstock, Nicholas R.;Ortiz-Rodríguez, Jessica C.;Perryman, Joseph T.;Sutton, Christopher;Velázquez, Jesús M.;Musgrave, Charles B.
• 通讯作者: Musgrave, Charles B.