Combined Machine Learning and Computational Chemistry Guided Discovery of Chevrel Phases for Electrocatalytic CO2 Reduction
Combined Machine Learning and Computational Chemistry Guided Discovery of Chevrel Phases for Electrocatalytic CO2 Reduction
批准号:
2016225
负责人:
Charles Musgrave
金额:
$37.54万
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
项目状态:
已结题
起止时间:
2020-09-01 至 2024-08-31
中文摘要
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,要么将其储存起来,要么将其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利用电力驱动二氧化碳与水的反应来产生新的化学物质。该反应可发生在具有适当催化性能的各种材料表面。最近,一种被称为Chevrels的有趣材料家族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,这类材料仍然相对未被研究,而且这种反应的效率仍然需要大幅提高才能变得经济。这项工作的目的是确定大量可能的雪佛兰的新材料,能够有效地将二氧化碳转化为有价值的产品。为这一反应找到更好的材料可以为减少大气中的二氧化碳水平和向可持续的未来过渡迈出重要的一步。由于现有的电化学CO2还原(eCO2R)催化剂选择性差和/或过电位高,电催化生产甲醇和C1+产物(含1碳原子的还原产物)仍然是一个重大的材料发现挑战。插层Chevrels (MyMo6X8, M = metal, X = S, Se, Te)是一种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选择性地从二氧化碳中产生甲醇,这表明插入式Chevrel相材料也可能是一类相对未被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设计和加速发现新的Chevrel相电催化剂,以实现高效和选择性的二氧化碳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eCO2R在溶剂和应用偏压下在Chevrel表面上的机制;2)开发催化剂稳定性、选择性和活性的机器学习描述子,从而能够合理、高通量地发现新的高性能Chevrel电催化剂,这些电催化剂使用地球上丰富的元素,将具有经济竞争力的二氧化碳转化为有价值的产品。本研究与本项目感兴趣的主题领域密切相关,包括与可再生能源相关的催化、电催化、关闭碳循环、二氧化碳的转化、新的催化剂设计和材料、对催化剂材料和机制的基本了解以及计算催化工具的进展。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。
英文摘要
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.
期刊论文(9)
专著(0)
科研奖励(0)
会议论文
登录
查看更多内容
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.
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.
Computationally Accelerated Discovery of Catalysts for Electrification of the Nitrogen Cycle
-
批准号:2400339
-
项目类别:Standard Grant
-
资助金额:$53.58万
-
财政年份:2024
-
负责人:Charles Musgrave
-
依托单位:
Automated Search for Materials for Ammonia Synthesis and Water Splitting
-
批准号:1806079
-
项目类别:Standard Grant
-
资助金额:$13.63万
-
财政年份:2018
-
负责人:Charles Musgrave
-
依托单位:
D3SC: Machine Learned Free Energies of Compounds
-
批准号:1800592
-
项目类别:Standard Grant
-
资助金额:$51.75万
-
财政年份:2018
-
负责人:Charles Musgrave
-
依托单位:
NSF/DOE Solar Hydrogen Fuel: Accelerated Discovery of Advanced RedOx Materials for Solar Thermal Water Splitting to Produce Renewable Hydrogen
-
批准号:1433521
-
项目类别:Standard Grant
-
资助金额:$52.54万
-
财政年份:2014
-
负责人:Charles Musgrave
-
依托单位:
Singlet Fission for Highly Efficient Organic Photovoltaics
-
批准号:1214131
-
项目类别:Continuing Grant
-
资助金额:$30.18万
-
财政年份:2012
-
负责人:Charles Musgrave
-
依托单位:
国内基金
海外基金
Understanding structural evolution of galaxies with machine learning
-
批准号:
-
项目类别:省市级项目
-
资助金额:10.0万元
-
批准年份:2022
-
负责人:Nicola Rosario Napolitano
-
依托单位: