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和教育，旨在了解合作机制，通过该机制，磁性材料从具有多种复杂相互作用的纳米级胶体组分中自组装，并在磁场，电场和光场的存在下。简明的表征和描述的条件下稳定的丰富的光谱自组装材料将提供必要的信息，其可重复的制备。只有通过对所有基本原理和微观现象的深刻理解，才能在自组装纳米级组件形成的材料领域取得巨大进展。该研究涉及开发和应用自组装材料建模的计算工具，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先进材料理论和建模技术的丰富教育经验。通过与实验学家的合作，这项研究将迭代地深入了解材料特性。在这种综合实验和计算方法中，学生将获得设计材料和技术的必要技能，旨在开发基于这些材料的新型器件。技术总结该奖项支持自组装超顺磁性纳米颗粒形成的磁性材料的计算和理论研究和教育。该研究需要PI小组开发多尺度建模工具，特别是能够描述相互作用的纳米颗粒的大尺度系统的高效Monte Carlo代码，其中组分之间的协同作用力将通过使用已知定律的分析计算，原子分子动力学模拟，和估计的耦合强度存在于所研究的系统在动态条件下形成的自组装。这些研究将侧重于澄清在自组装过程中所发挥的作用，通过不同长度尺度的纳米粒子之间的作用力，选择不同材料的影响，纳米粒子的大小和形状，它们的配体和溶剂，整体充电，以及外部磁场，电场和光场。该建模方法将揭示纳米颗粒链、带、条、簇和螺旋的空间和磁性结构，以及实验中观察到的它们排列的起源。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分的实验制备材料的知识，这些纳米级组分具有复杂的相互作用，导致许多可能的最终结构可通过外部磁场进行调节。这些材料将为未来几代具有众多应用的设备提供丰富的平台。与本研究相关的活动将为研究生和本科生提供先进材料理论和建模技术的丰富教育经验。通过与实验学家的合作，这项研究将迭代地深入了解材料特性。

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.