Designing Novel Tunable Colloids Via Inverse Statistical Mechanics

CBET -1701843 PI：Torquato，Salvatore许多先进材料的设计依赖于设计构建模块的能力，例如胶体和聚合物，它们以特定的方式相互作用，以自组织和形成具有新的电子，机械或光学特性的材料。科学家和工程师通过指定构建块之间的相互作用势来预测这一过程，然后计算自组织产生的材料结构及其属性。 该奖项将支持理论和计算研究，以发现使用相反或“逆”方法的新材料。 在逆向方法中，设计人员首先指定所需的材料结构和特性，然后发现将产生所需结构的构建块之间所需的相互作用。该项目将强调通过结合当前可用的胶体相互作用来发现可以通过实验实现的相互作用。 因此，研究结果可以为实验人员制备设计胶体提供指导。逆方法将提供新的方法来控制材料的有序/无序程度，以实现新的性能，这将加速通过设计发现材料。该项目将支持研究生的培训，并为本科生提供参与研究的机会。该研究产生的算法将免费提供给研究人员，并将用于加强学生教育。该奖项支持通过逆几何力学优化技术进行材料发现的理论和计算研究。逆电磁力学方法将制定和应用于优化多粒子相互作用，以实现所需的物理性能的目标胶体结构。逆弹性力学方法允许对物质凝聚相的结构和物理性质进行新的思考模式，并且非常适合通过设计发现材料。然而，迄今为止，逆技术已经考虑了不受实验可实现的约束的潜在函数。该项目的一个中心目标是优化各向同性和各向异性对相互作用，这些相互作用受到目前可用的胶体排斥和吸引相互作用的组合的约束，包括排除体积排斥，耗尽相互作用，货车范德华力，通过官能化颗粒表面诱导的力，和静电排斥。潜在的功能将被“定制”，以实现强大的自组装独特的目标晶体，液体和非晶态物质，包括外来无序超均匀系统。将被设计的新型材料的例子包括那些用于光子学和颜色传感应用，负或消失的热膨胀系数，可调的负和正泊松比，最佳的传输，电磁和机械性能，以及用于催化，分离，传感器和电子应用的中孔固体。

CBET - 1701843PI: Torquato, SalvatoreThe 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.