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.

向大气中排放二氧化碳是气候变化的主要驱动力，通往可持续未来的道路将在很大程度上依赖于从空气中去除二氧化碳，并将其储存或转化为燃料或其他有价值的化学品。实现这一目标的一个有希望的途径是用电来驱动二氧化碳与水的反应，以产生新的化学物质。这种反应可以发生在具有适当催化性能的各种材料的表面上。最近，一个有趣的材料家族被称为雪佛兰被证明可以将二氧化碳转化为燃料。然而，尽管这些有希望的初步结果，这一系列的材料仍然相对未研究，该反应的效率仍然需要大幅提高，成为经济。这项工作的目的是确定大量可能的雪佛兰新材料，能够有效地将二氧化碳转化为有价值的产品。鉴定用于该反应的上级材料可以为降低大气中的二氧化碳水平和向可持续的未来过渡迈出重要一步。由于现有电化学CO2还原（eCO 2 R）催化剂的选择性差和/或过电位高，甲醇和C1+产物（具有1个碳原子的还原产物）的电催化生产仍然是一个重大的材料发现挑战。插层Chevrels（MyMo 6X 8，M =金属，X = S，Se，Te）是一类有前途但相对未开发的材料，与钙钛矿一样，为材料设计和发现提供了高度可调的框架，具有广泛的潜在应用。此外，它们最近被证明可以从CO2中选择性地生产甲醇，这表明插层Chevrel相材料也可能是一类相对未开发的有前途的电催化剂，可以调整催化性能。该项目的目标是通过计算分析和指导新的Chevrel相电催化剂的设计和加速发现，以有效和选择性地将CO2转化为有价值的产品。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在溶剂中和在施加的偏压下Chevrel表面上的eCO 2 R的机制，以及2）开发催化剂稳定性、选择性和活性的机器学习描述符，高通量发现新型高性能Chevrel电催化剂，该催化剂采用地球上丰富的元素，将具有经济竞争力的CO2转化为有价值的产品。这项研究与该计划感兴趣的主题领域密切相关，包括可再生能源相关的催化，电催化，关闭碳循环，二氧化碳转化，新的催化剂设计和材料，该奖项反映了NSF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。

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.