| 项目编号: | 1604984
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| 项目名称: | Accelerating Multimetallic Catalyst Design for Electrochemical CO2 Reduction using Quantum Chemical Modeling and Machine Learning |
| 作者: | Hongliang Xin
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| 承担单位: | Virginia Polytechnic Institute and State University
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| 批准年: | 2016
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| 开始日期: | 2016-07-01
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| 结束日期: | 2019-06-30
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| 资助金额: | 386942
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| 资助来源: | US-NSF
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| 项目类别: | Standard Grant
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| 国家: | US
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| 语种: | 英语
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| 特色学科分类: | Engineering - Chemical, Bioengineering, Environmental, and Transport Systems
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| 英文关键词: | co2
; machine-learning
; electrochemical reduction
; co2 reduction
; useful chemical
; greenhouse gas co2
; catalyst discovery
; machine-learning model
; advanced machine-learning algorithm
; electrochemical conversion process
; efficient electrochemical conversion
; value-added chemical
|
| 英文摘要: | 1604984
Xin, Hongliang
The 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. |
| 资源类型: | 项目
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/91799
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| Appears in Collections: | 全球变化的国际研究计划
科学计划与规划
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| Recommended Citation: | |
Hongliang Xin. Accelerating Multimetallic Catalyst Design for Electrochemical CO2 Reduction using Quantum Chemical Modeling and Machine Learning. 2016-01-01.
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