Multiscale Modeling of Chiral Self-assemblies of Superparamagnetic Nanoparticles

非技术总结该奖项支持计算和理论研究及教育，旨在了解磁性材料在存在磁场、电场和光场的情况下，通过具有多个复杂相互作用的纳米级胶体组件自组装的合作机制。对丰富的自组装材料光谱稳定的基本条件的简明描述和描述将为它们的可重复制备提供必要的信息。只有通过对所有基本原理和微观现象的扎实理解，才能在自组装纳米组件形成的材料领域取得重大进展。这项研究涉及开发和应用计算工具来对自组装材料进行建模，这些工具将与更广泛的计算材料科学界共享。与本研究相关的活动将为研究生和本科生提供丰富的先进材料理论和建模技术的教育经验。通过与实验者的合作，这项研究将反复进行，以深入了解材料的性质。在这种综合的实验和计算方法中，学生将获得必要的技能来设计材料和旨在开发基于这些材料的新型设备的技术。技术总结该奖项支持由自组装的超顺磁性纳米颗粒形成的磁性材料的计算和理论研究以及教学。这项研究需要在PI组中开发多尺度建模工具，特别是能够描述大规模相互作用纳米粒子系统的高效蒙特卡罗代码，其中，通过使用已知定律的分析计算、原子分子动力学模拟以及在自组装过程中形成的动力学条件下对所研究系统中存在的耦合强度的估计，组分间的协作力将被参数化。这些研究将集中于阐明不同长度尺度的纳米粒子之间的作用力在自组装过程中所起的作用，选择不同的材料、纳米粒子的大小和形状、它们的配体和溶剂、总电荷以及外部磁场、电场和光场的影响。这种建模方法将揭示纳米粒子链、带、条、簇和螺旋的空间和磁性结构，以及它们在实验中观察到的排列的来源。通过开发新的建模方法，该项目将推进基于自组装纳米级组件的实验制备材料的知识，这些组件具有复杂的相互作用，导致许多可能的最终结构可以通过外场调节。这些材料将为拥有众多应用的未来几代设备形成丰富的平台。与本研究相关的活动将为研究生和本科生提供丰富的先进材料理论和建模技术的教育经验。通过与实验者的合作，这项研究将反复进行，以深入了解材料的性质。

NON-TECHNICAL SUMMARYThis award supports computational and theoretical research and education aimed at understanding cooperative mechanisms by which magnetic materials self-assemble from nanoscale colloidal components with multiple complex interactions and in the presence of magnetic, electric, and optical fields. Concise characterization and description of conditions underlying the stabilization of a rich spectrum of self-assembled materials will provide information necessary for their reproducible preparation. Dramatic progress in the area of materials formed by self-assembled nanoscale components can proceed only through a solid understanding of all underlying principles and microscopic phenomena. The research involves developing and applying computational tools for modeling of self-assembled materials, which will be shared with the broader computational materials science community. Activities associated with this research will provide rich educational experiences for graduate and undergraduate students in advanced materials theory and modeling techniques. Through collaboration with experimentalists, this research will iteratively proceed towards a deep understanding of materials properties. In this integrated experimental and computational approach, the students will gain the necessary skills to design materials and technologies aiming at development of novel devices based on these materials.TECHNICAL SUMMARYThis award supports computational and theoretical research and education of magnetic materials formed by self-assembled superparamagnetic nanoparticles. The research necessitates development of multiscale modeling tools in the PI's group, in particular, efficient Monte Carlo codes able to describe large scale systems of interacting nanoparticles, where cooperative forces acting between the constituents will be parametrized by analytical calculations using known laws, atomistic molecular dynamics simulations, and estimates of coupling strengths present in the studied systems under dynamical conditions formed during the self-assembly. These studies will focus on clarifying the roles played during the self-assembly by forces acting between the nanoparticles at different length scales, the effects of choosing different materials, sizes and shapes of the nanoparticles, their ligands and solvents, overall charging, and external magnetic, electric, and optical fields. This modeling approach should reveal the spatial and magnetic structures of nanoparticle chains, ribbons, stripes, clusters, and helices, and the origins of their arrangements observed in experiments.Through development of novel modeling methods, the project will advance the knowledge of experimentally prepared materials based on self-assembled nanoscale components with complex interactions leading to numerous possible final structures tunable by external fields. These materials will form a rich platform for future generations of devices with numerous applications. Activities associated with this research will provide rich educational experiences for graduate and undergraduate students in advanced materials theory and modeling techniques. Through collaboration with experimentalists, this research will iteratively proceed towards a deep understanding of materials properties.