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

向大气中排放二氧化碳是气候变化的主要驱动因素，通往可持续未来的道路将在很大程度上依赖于从空气中消除二氧化碳，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利用电力驱动二氧化碳和水的反应，以产生新的化学品。该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，一种被称为雪佛兰的有趣的材料家族被证明可以将二氧化碳转化为燃料。然而，尽管有这些有希望的初步结果，这类材料仍然相对缺乏研究，这种反应的效率仍然需要实质性的改进才能变得经济。这项工作的目标是确定大量可能的雪佛兰新材料，这些材料能够有效地将二氧化碳转化为有价值的产品。为这一反应确定优良的材料可以为降低大气中的二氧化碳水平和过渡到可持续的未来提供重要的一步。由于现有的电化学二氧化碳还原(ECO2R)催化剂的低选择性和/或高过电位，电催化生产甲醇和C_1+产物(1个碳原子的还原产物)仍然是材料发现的重大挑战。插层雪佛兰(MyMo6X8，M=金属，X=S，Se，Te)是一类很有前途但相对未被开发的材料，与钙钛矿一样，为材料设计和发现提供了一个高度可调的框架，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生成甲醇，这表明插层Chevrel相材料也可能是一类相对未被探索的有前途的电催化剂，可以根据催化性能进行调整。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雪佛兰相电催化剂，以有效和选择性地将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在溶剂中和施加偏压下在雪佛兰表面上的机理，以及2)开发机器学习的催化剂稳定性、选择性和活性描述符，使合理、高通量地发现新型高性能雪佛兰电催化剂，这些催化剂使用丰富的稀土元素将具有经济竞争力的二氧化碳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，包括与可再生能源相关的催化、电催化、关闭碳循环、二氧化碳转化、新型催化剂设计和材料、对催化剂材料和机理的基本了解以及计算催化工具的进展。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.