| 项目编号: | 1701843
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| 项目名称: | Designing Novel Tunable Colloids Via Inverse Statistical Mechanics |
| 作者: | Salvatore Torquato
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| 承担单位: | Princeton University
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| 批准年: | 2017
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| 开始日期: | 2017-07-01
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| 结束日期: | 2020-06-30
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| 资助金额: | 423413
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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: | statistical-mechanical
; inverse approach
; inverse statistical-mechanical method
; designer colloid
; available colloidal interaction
; material
; inverse technique
; project
; colloidal structure
; inverse statistical-mechanical optimization technique
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| 英文摘要: | CBET - 1701843
PI: Torquato, Salvatore
The design of many advanced materials relies on the ability to devise building blocks, such as colloids and polymers, that interact with each other in specific ways to self-organize and form materials with novel electronic, mechanical or optical properties. Scientists and engineers predict this process by specifying an interaction potential between the building blocks and then computing the material structure and its properties that arise from self-organization. This award will support theoretical and computational research to discover new materials using the opposite or "inverse" approach. In the inverse approach, designers start by specifying the desired material structure and properties and then discover the required interactions between the building blocks that will produce the desired structure The project will emphasize finding interactions that can be realized experimentally by combining currently available colloidal interactions. Hence, the results should provide guidance to experimentalists to fabricate designer colloids. The inverse approach will provide new ways to control the degree of order/disorder of the material to achieve novel properties, which will accelerate the discovery of materials by design. The project will support training of graduate students and will provide opportunities for undergraduates to participate in research. Algorithms resulting from the research will be made freely available to researchers and will be used to enhance student education.
This award supports theoretical and computational research for materials discovery by inverse statistical-mechanical optimization techniques. Inverse statistical-mechanical methods will be formulated and applied to optimize many-particle interactions to achieve targeted colloidal structures with desired physical properties. Inverse statistical-mechanical methods allow for a new mode of thinking about the structure and physical properties of condensed phases of matter and are ideally suited for materials discovery by design. However, inverse techniques thus far have considered potential functions that were not constrained to be experimentally realizable. A central aim of this project is to optimize both isotropic and anisotropic pair interactions that are constrained by what can be achieved by a combination of currently available colloidal repulsive and attractive interactions, including excluded-volume repulsions, depletion interactions, Van der Waals forces, forces induced by functionalizing the particle surface, and electrostatic repulsions. Potential functions will be "tailored" to achieve robust self-assembly of unique targeted crystal, liquid and amorphous states of matter, including exotic disordered hyperuniform systems. Examples of novel materials that will be designed include those for photonics and color sensing applications, negative or vanishing thermal expansion coefficients, tunable negative and positive Poisson ratios, optimal transport, electromagnetic and mechanical properties, and mesoporous solids for applications in catalysis, separations, sensors and electronics. |
| 资源类型: | 项目
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/89891
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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: | |
Salvatore Torquato. Designing Novel Tunable Colloids Via Inverse Statistical Mechanics. 2017-01-01.
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