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

该奖项支持理论和计算研究，旨在设计出丰富的环境友好型材料，用作催化剂，将水分子分解为氢和氧组成元素，并以各种形式捕获碳，使其成为环境友好型。催化剂是一种可以加速特定化学反应速率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。在此过程中，PI还将开发新的计算方法，旨在以更高的现实主义水平准确预测这些多组分催化系统，而不是从组成原子到宏观尺度。成功将使设计能源储存和转换技术的新方法成为可能。这些新工具还可应用于设计各种其他可再生能源技术，如太阳能电池、燃料电池和可将热量转化为电能的热电材料，以及先进的光学和电子技术。作为这项研究的结果，计算方法和生成的数据将免费提供，为在线教育提供新的机会，并为研究人员和行业加速从材料设计到设备实现的途径。这项研究将为教育活动提供一个平台，包括为学生提供研究机会，通过使用数据丰富课程，以及特别努力吸引和指导女性和残疾学生在各种情况下追求科学和工程事业。技术总结该奖项支持理论和计算研究和教育，以提高使用计算设计具有所需特性的材料的能力重点是材料表面及其作为催化剂的应用。设计用于析氧反应和氧还原反应的活性、稳定、地球上丰富的催化剂将使电化学能量储存和转换技术（例如电解槽、燃料电池和金属-空气电池）能够有效发展。类似地，需要具有高稳定性、高活性和高选择性的新的地球丰富的催化剂材料，以实现基于水性电化学二氧化碳还原反应的技术，该技术可以产生氢气、甲烷、甲醇和潜在的长链烃，用作燃料或特种化学品。催化剂的活性在很大程度上取决于表面性质，并且预测和理解现实催化界面处的结构-功能关系的能力超出了当前的方法。这对设计此类材料提出了重大挑战。在本项目中，PI将通过开发一种新方法来解决这一关键挑战，该方法旨在快速准确地预测现实催化剂-溶剂界面处的原子和电子结构。该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的组合，将能够在明确的水分子存在下研究纳米结构材料中的环境-结构-性质关系，以及基本预测模型的开发，以指导具有定制特性的新材料系统的设计。这些能力将在调查析氧反应，氧还原反应和二氧化碳还原反应的背景下，从有前途的过渡金属氧化物类的候选材料证明。 这项研究有可能导致新型水分解和二氧化碳还原催化剂的设计;对氧化物界面化学的基本见解的发展;以及新的计算工具的传播，这将使复杂的，现实的界面结构的详细研究和预测具有量子力学的准确性。物理见解和新工具都将直接适用于设计其他催化反应的定制材料系统，以及各种其他应用，如光化学，燃料电池和电池，热电和纳米级复合材料，其中界面起着重要作用。此外，所提出的方法可能会导致一个新的范例，在高通量计算筛选材料系统，使材料的选择与属性，直接相关的材料纳入现实的设备几何形状。 计算方法和这项研究产生的数据将在网上免费提供，为在线教育和加速从材料设计到应用的途径提供新的机会。这项研究将为教育部分活动提供一个平台，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情况下从事科学和工程职业的女性和残疾学生的参与和指导。

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.