| 项目编号: | 1553365
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| 项目名称: | CAREER: Accurate Electrochemical Barriers Accelerated by Machine-learning |
| 作者: | Andrew Peterson
|
| 承担单位: | Brown University
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| 批准年: | 2016
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| 开始日期: | 2016-01-01
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| 结束日期: | 2020-12-31
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| 资助金额: | 560001
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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: | machine-learning
; machine-learning tool
; development
; atom-centered machine learning
; student
; machine learning
|
| 英文摘要: | Abstract (Peterson;1553365)
The study will promote advances in theoretical understanding of electrocatalysis as accelerated by machine-learning tools. The resulting understanding will aid the development of related technologies such as solar-fuel devices, batteries, fuel cells and electrolyzers - all of relevance to renewable energy and the commercialization of sustainable technologies. The research will also provide educational opportunities to students at various levels in both the U.S. and in rural Kenya, and will facilitate introduction of high-fidelity, accelerated atomistic calculations across a broad research community via the principal investigator's publicly available code, "Amp".
Electronic structure theory has revolutionized heterogeneous catalyst design in recent years, however it has had greater challenges in electrocatalysis due to the difficulty of calculating transition state energy barriers (which often dictate catalytic performance). This study will provide the first systematic study of such potential-dependent electrocatalytic barriers across a range of catalytic materials and adsorbed reactants, thus facilitating the discovery of new materials and electrocatalytic processes. The transition-state calculations will be enabled by the development of unique, atom-centered, machine-learning tools that dramatically accelerate atomistic calculations while matching the accuracy of computationally-intensive (and often exceedingly time-consuming) "ab initio" calculations. The PI's Amp software modularizes atom-centered machine learning, and will be used to accelerate the search of potential energy surfaces for local minima, transition states, and global minima. Moreover, the acceleration provided by machine learning will also facilitate the introduction of complicated phenomena associated with the electrochemical environment such as solvent effects and large unit cells of typical materials. More broadly, the project will provide information that will be used by the PI as teaching tools to convey reaction visualization to students ranging from local high school and Brown undergraduate and graduate students to students at a new science and technology university (JOOUST) in rural Kenya. Moreover, continued development of Amp, with new shared applications, will accelerate materials discovery across a broad range of applications related to energy and the environment. |
| 资源类型: | 项目
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/92936
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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: | |
Andrew Peterson. CAREER: Accurate Electrochemical Barriers Accelerated by Machine-learning. 2016-01-01.
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