CAREER: Development of Fundamental Relationships Between Surface Structure, Composition, Stability, and Activity of Oxide Electrocatalysts in Aqueous Environments

非技术总结该奖项支持理论和计算研究，旨在使丰富和环境友好的材料的设计能够用作催化剂，将水分子分解为其组成的氢和氧组成元素，并捕获各种形式的碳，使其对环境无害。催化剂是一种可以加速特定化学反应速度而不会在反应中消耗的材料。PI将使用一套计算工具来研究和设计新型催化剂。在这一过程中，PI还将开发新的计算方法，旨在实现对这些多组分催化体系的准确预测，其真实性高于目前从组成原子到宏观尺度的预测水平。成功将使设计储能和转换技术的新方法成为可能。这些新工具还可能应用于其他一系列可再生能源技术的设计，如太阳能电池、燃料电池和可以将热能转换为电能的热电材料，以及先进的光学和电子技术。这项研究产生的计算方法和数据将免费提供，为在线教育以及研究人员和行业提供新的机会，以加快从材料设计到设备实施的过程。这项研究将为教育组成部分的活动提供一个平台，包括为学生提供研究机会，通过使用数据丰富课程，以及在各种背景下吸引和指导女性和残疾学生在科学和工程领域的职业生涯。技术总结该奖项支持理论和计算研究和教育，以提高利用计算设计具有所需性能的材料的能力，重点是材料表面及其作为催化剂的应用。设计出活性好、稳定性好、富含稀土元素的析氧反应和氧还原反应催化剂，将使电解槽、燃料电池、金属-空气电池等电化学储能和转化技术得到有效发展。同样，需要具有高稳定性、高活性和高选择性的富含地球的新型催化剂材料来支持基于水中二氧化碳电化学还原反应的技术，该反应可以产生氢、甲烷、甲醇，以及可能用作燃料或特种化学品的较长链烃。催化剂的活性在很大程度上是由表面性质决定的，预测和理解现实催化界面上的结构-功能关系的能力超出了目前的方法。在这个项目中，PI将通过开发一种新的方法来解决这一关键挑战，该方法旨在快速和准确地预测现实催化剂-溶剂界面的原子和电子结构。这种方法基于第一性原理密度泛函理论计算、机器学习算法、经典分子动力学和蒙特卡罗模拟以及电化学原理，将使我们能够在显式水分子存在的情况下研究纳米材料中的环境-结构-性质关系，以及开发基本预测模型来指导具有定制性能的新材料体系的设计。这些能力将在研究过渡金属氧化物候选材料上的放氧反应、氧还原反应和二氧化碳还原反应的背景下得到展示。这项研究有可能导致设计新型的水分解和二氧化碳还原催化剂；发展对氧化物界面化学的基本见解；以及传播新的计算工具，使详细研究和预测具有量子力学精度的复杂、现实的界面结构成为可能。物理见解和新工具都将直接适用于为其他催化反应量身定做的材料系统的设计，以及各种其他应用，如光伏、燃料电池和电池、热电和纳米复合材料，其中界面发挥着重要作用。此外，所提出的方法可能导致材料系统的高通量计算筛选的新范例，通过允许相对于与材料并入现实设备几何形状的材料直接相关的属性进行材料选择。计算方法和这项研究产生的数据将在网上免费提供，为在线教育和加快从材料设计到应用的道路提供新的机会。这项研究将为教育组成部分的活动提供一个平台，包括为学生提供研究机会，通过使用数据丰富课程，以及特别努力在各种背景下吸引和指导女性和残疾学生在科学和工程领域就业。

NONTECHNICAL SUMMARY This award supports theoretical and computational research with the aim to enable the design of abundant and environmentally benign materials to be utilized as catalysts for splitting water molecules into their constituent hydrogen and oxygen component elements, and for capturing carbon in various forms to render it environmentally benign. A catalyst is a material that can accelerate the rate of a specific chemical reaction without being consumed in the reaction. The PI will use a suite of computational tools to investigate and design novel catalysts. In the process, the PI will also develop new computational approaches that are aimed to enable accurate prediction of these multi-component catalytic systems at a higher level of realism than currently possible starting from the constituent atoms up to the macroscopic scale. Success will enable new ways to design energy storage and conversion technologies. These new tools may also have application to the design of a wide range of other renewable energy technologies such as solar cells, fuel cells, and thermoelectric materials that can convert heat to electricity, as well as of advanced optics and electronics technologies. The computational approaches and the data generated as a result of this research will be made freely available providing new opportunities for online education and for researchers and industry to accelerate the pathway from materials design to device implementation. The research will provide a platform for educational component activities including research opportunities for students, course enrichment through use of data, and special efforts to engage and mentor both female and disabled students in various contexts pursuing careers in science and engineering.TECHNICAL SUMMARYThis award supports theoretical and computational research and education to advance toward the capability to design materials with desired properties using computation with a focus on materials surfaces and their application as catalysts. The design of active, stable, earth-abundant catalysts for the oxygen evolution reaction and the oxygen reduction reaction would enable effective development of electrochemical energy storage and conversion technologies such as electrolyzers, fuel cells, and metal-air batteries. Similarly, new earth-abundant catalyst materials with high stability, high activity, and high selectivity are required to enable technologies based on aqueous electrochemical carbon dioxide reduction reactions, which can produce hydrogen, methane, methanol, and potentially longer-chain hydrocarbons for use as fuels or specialty chemicals. Catalyst activity is largely governed by surface properties, and the ability to predict and understand, structure-function relationships at realistic catalytic interfaces lies beyond current approaches. This presents a significant challenge to designing such materials.In this project, the PI will address this key challenge by developing a new approach that aims for rapid and accurate prediction of atomic and electronic structure at realistic catalyst-solvent interfaces. The approach, based on a combination of first-principles density functional theory computations, machine learning algorithms, classical molecular dynamics and Monte Carlo simulations, and electrochemical principles will enable the study of environment-structure-property relationships in nanostructured materials in the presence of explicit water molecules, as well as the development of fundamental predictive models to guide the design of new materials systems with tailored properties. These capabilities will be demonstrated in the context of investigating the oxygen evolution reaction, the oxygen reduction reaction, and carbon dioxide reduction reactions on candidate materials from the promising class of transition metal oxides. This research has the potential to lead to the design of novel water splitting and carbon dioxide reduction catalysts; the development of fundamental insights into oxide interface chemistry; and the dissemination of new computational tools that will enable detailed study and prediction of complex, realistic interface structures with quantum mechanical accuracy. Both the physical insights and the new tools will be directly applicable to the design of tailored materials systems for other catalytic reactions, as well as for a wide variety of other applications, such as photovoltaics, fuel cells and batteries, thermoelectrics, and nanoscale composite materials, in which interfaces play an important role. In addition, the proposed methodology could lead to a new paradigm in high-throughput computational screening of materials systems by enabling materials selection with respect to properties that are directly related to materials incorporation into realistic device geometries. Computational approaches and the data generated as a result of this research will be made freely available online, providing new opportunities for online education and for accelerating the pathway from materials design to application. The research will provide a platform for educational component activities including research opportunities for students, course enrichment through use of data, and special efforts to engage and mentor both female and disabled students in various contexts pursuing careers in science and engineering.