| 项目编号: | 1600218
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| 项目名称: | Accelerated Dynamics of Surface Chemical Reactions |
| 作者: | Ramamurthy Ramprasad
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| 承担单位: | University of Connecticut
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| 批准年: | 2016
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| 开始日期: | 2016-09-01
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| 结束日期: | 2019-08-31
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| 资助金额: | 300000
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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: | surface adatom diffusion
; surface chemical reaction
; important model surface science problem
; surface catalytic reaction
; surface oxidation
; accelerated dynamics
; force-field
; molecular dynamics
; surface chemical reactionssurface chemical reaction
; energy
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| 英文摘要: | 1600218 PI: Ramprasad
Institution: University of Connecticut
Title: Accelerated Dynamics of Surface Chemical Reactions
Surface chemical reactions are ubiquitous in natural phenomena and play a key role in several scientific disciplines, such as heterogeneous catalysis, crystal growth, and electrochemistry. Achieving a molecular-level picture of the mechanisms and dynamical detail of surface chemical reactions though remains a daunting task. Current methods of inquiry, be it empirical or computational, provide us with only a limited view of this complex world. One way of achieving the requisite level of understanding is by the use of molecular dynamics (MD) simulations. These simulations can directly monitor the progression of reactions at surfaces at the molecular level as a function of a variety of relevant conditions. Nevertheless, significant gaps remain in present-day MD capabilities: they are either fast (but not versatile or accurate), e.g., those based on empirical force-fields, or they are versatile and accurate (but not efficient), e.g., those based on quantum mechanical (or ab initio) methods. The proposed work will exploit an adaptive machine learning scheme that can both accelerate ab initio MD simulations as well as create accurate force-fields (at no extra cost) on-the-fly. Timescales previously unreachable using quantum mechanical simulations may be accessed using this new paradigm (conceivably,milliseconds to seconds), while still preserving the fidelity of quantum mechanics.
The primary reason ab-initio MD simulations are slow is largely because of enormous redundancies that permeate present-day paradigms. Energies and forces, the ingredients necessary to perform MD simulations, are evaluated for every configuration that is visited, regardless of whether a new configuration is similar to a previously visited configuration. The basic premise underlying this proposal is that a methodology based on machine learning can be used to eliminate the significant effort involved in predicting atomic forces and energies of revisited or similar states within a local minimum, and at equivalent multiple local minima, thus eliminating an enormous amount of redundancies. Only when a truly new configuration is encountered is an ab initio scheme necessary; otherwise, the inexpensive machine learning algorithm is used to predict atomic forces and energies. In the former instance, the learning algorithm is retrained to include the new information, thus making the prediction scheme adaptive on-the-fly. The specific goals of the proposed research are: (1) The development of stand-alone force-fields just using the data accumulated during ab initio MD simulations; and (2) Application of this development to important model surface science problems, including surface adatom diffusion, surface oxidation, and surface catalytic reactions. Several educational initiatives are also planned, including new course offerings, workshops, online lectures, short courses, and symposia. |
| 资源类型: | 项目
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/91001
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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: | |
Ramamurthy Ramprasad. Accelerated Dynamics of Surface Chemical Reactions. 2016-01-01.
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