Accelerating Multimetallic Catalyst Design for Electrochemical CO2 Reduction using Quantum Chemical Modeling and Machine Learning

使用量子化学建模和机器学习加速电化学二氧化碳还原的多金属催化剂设计

• 批准号: 1604984
• 负责人: Hongliang Xin
• 金额: $ 38.09万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1604984&HistoricalAwards=false
• 关键词: Accelerating; Multimetallic; Catalyst; Design; Electrochemical

1604984Xin, HongliangThe proposed work is a computational study aimed at identifying novel multimetallic nanomaterials for the efficient electrochemical conversion of carbon dioxide (CO2) to value-added chemicals and fuels. This has the dual benefit of reducing the emissions of the greenhouse gas CO2 and moving closer to a sustainable energy future based on a closed loop carbon cycle fueled by a combination of solar energy and electrochemical conversion processes. Prior research has demonstrated that copper (Cu) nanocubes exhibit remarkable selectivity towards carbon-carbon bond formation, but with electrical efficiency too low to be commercially viable. The study is based on the hypothesis that multimetallic nanocubes consisting of precisely mixed, low-cost metals can convert CO2 to useful chemicals and fuels at higher efficiency and selectivity than the Cu nanocubes alone. The researchers bring together expertise in density functional theory calculations and ab initio molecular dynamics - aided by advanced machine-learning algorithms - to predict materials combinations that lower the over-potential for electrochemical reduction of CO2 to ethylene and ethanol. The research is based on a three-step approach that first unravels the active site and reaction mechanism of CO2 reduction on Cu nanocubes, then creates predictive models linking nanoparticle composition and structure to the surface reactivity by machine-learning models, and lastly, develops an integrated framework for accelerating catalyst discovery. The broader impact of the work will be enhanced through educational outreach activities and open-source access to the tools developed during the course of the project.

1604984 Xin，Hongliang拟议的工作是一项计算研究，旨在识别新型多金属纳米材料，用于二氧化碳（CO2）的有效电化学转化为增值化学品和燃料。这具有减少温室气体CO2的排放和基于由太阳能和电化学转换过程的组合提供燃料的闭环碳循环的可持续能源未来的双重好处。先前的研究已经证明，铜（Cu）纳米立方体对碳-碳键形成表现出显着的选择性，但电效率太低而不能商业化。该研究基于这样的假设，即由精确混合的低成本金属组成的多金属纳米立方体可以比单独的Cu纳米立方体更高的效率和选择性将CO2转化为有用的化学品和燃料。研究人员将密度泛函理论计算和从头算分子动力学方面的专业知识结合在一起-在先进的机器学习算法的帮助下-预测材料组合，以降低CO2电化学还原为乙烯和乙醇的过电位。该研究基于三步方法，首先揭示Cu纳米立方体上CO2还原的活性位点和反应机制，然后通过机器学习模型创建将纳米颗粒组成和结构与表面反应性联系起来的预测模型，最后开发一个加速催化剂发现的综合框架。将通过教育外联活动和开放源代码获取项目期间开发的工具，加强这项工作的广泛影响。