Collaborative Research: Concurrent Design of Quasi-Random Nanostructured Material Systems (NMS) and Nanofabrication Processes using Spectral Density Function

该奖项支持跨学科的研究，以创建一个新的并行设计框架，统一功能性准随机纳米/微米结构的设计和纳米纤维工艺的设计，以加速具有上级性能的稳健纳米结构材料系统的开发。 准随机纳米结构在开发具有各种功能的先进材料系统中发挥着越来越重要的作用。 这些结构不包括周期性重复的相同的单元格，但表面上随机的材料分布与潜在的空间相关性。 与通常需要昂贵和耗时的制造的周期性设计相比，准随机纳米结构可以通过低成本和可扩展的制造工艺合成。 然而，由于缺乏适当的计算设计范例，往往是一个不匹配的微结构设计和可行的纳米制造技术。 虽然测试平台的重点是有机光伏电池，这项研究将建立广泛的微结构系统的特性/性能的兴趣主要取决于空间的相关性，而不是局部几何结构的方法的适用性。潜在的工业和军事应用范围广泛，包括生物医疗设备，超强材料，消费电子，光子学和使用纳米级结构/设备的电信。 该研究还为设计，纳米制造，材料和力学领域的研究人员提供了独特的合作经验。 研究将通过跨大学的教学和评估，并共同定位和共同建议的学生training.The并行设计方法创建了一个从现有的确定性计算材料工程的非确定性的微结构设计，是自下而上的纳米制造过程的内在随机性兼容的转变与教育相结合。关键的新奇是使用物理感知的谱密度函数，一种非确定性的微观结构表示，作为工程材料系统设计中加工结构和结构性能映射之间的联系。该方法有利于快速探索可行的和兼容的处理和结构解决方案，大大降低了设计维度。 一个原子分辨率的高性能计算探索将实现使用粗粒度的分子动力学，以了解基本的传输机制的基础上的材料加工依赖的结构形态的演变。 自下而上的制造过程和多尺度成像技术将为验证方法、校准和验证计算结果以及深入的科学发现提供平台。最后，除了准随机纳米结构的固有鲁棒性之外，该方法提供了鲁棒的纳米结构材料系统设计，不仅考虑了加工条件的变化，而且考虑了计算机模型本身的不确定性。

This award supports an interdisciplinary research to create a novel concurrent design framework that unifies the design of functional quasi-random nano/microstructures and the design of nanofabrication processes to accelerate the development of robust nanostructured material systems with superior performance. Quasi-random nanostructures are playing an increasingly important role in developing advanced material systems with various functionalities. These structures comprise no periodic repetition of identical unit-cells but a seemingly random material distribution with underlying spatial correlation. Compared to periodic designs that usually require expensive and time intensive fabrications, quasi-random nanostructures can be synthesized by low-cost and scalable manufacturing processes. However, due to the lack of appropriate computational design paradigms, there is often a mismatch between microstructure designs and feasible nanofabrication techniques. Although the testbed is focused on organic photovoltaic cells, this research will establish the applicability of the approach for a wide range of microstructural systems where the properties/performance of interest mainly depends on the spatial correlations instead of local geometries of microstructures. The broad range of potential industrial and military applications includes bio-medical devices, ultra-strong materials, consumer electronics, photonics, and telecommunications using nano-scale structures/devices. The research also offers unique collaborative experiences for researchers across the fields of design, nanomanufacturing, materials, and mechanics. Research will be integrated with education through cross-university teaching and assessment, and co-located and co-advised student training.The concurrent design approach creates a shift from existing deterministic computational materials engineering to non-deterministic microstructure design that is compatible with the intrinsic stochasticity of bottom-up nanomanufacturing processes. The key novelty is to use the physics-aware Spectral Density Function, a non-deterministic microstructure representation, as the link between the processing-structure and the structure-performance mappings in the design of engineered material systems. The approach facilitates rapid exploration of feasible and compatible processing and structure solutions, with a significantly reduced design dimensionality. An atomic resolution high-performance computational exploration will be achieved using coarse grained molecular dynamics to understand the fundamental transport mechanisms based on the materials processing dependent evolution of the structural morphologies. The bottom-up fabrication processes and multiscale imaging techniques created will offer a platform for validation of the approach, calibration and validation of computational findings, and deep scientific discovery. Finally, in addition to the intrinsic robustness of quasi-random nanostructures, the approach offers robust nanostructured material systems designs considering not only the variations in processing conditions but also the uncertainty of the computer model itself.