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

该奖项支持一项跨学科研究，旨在创建一种新的并行设计框架，该框架将功能准随机纳米/微结构的设计与纳米制造工艺的设计结合起来，以加速具有卓越性能的坚固纳米结构材料系统的开发。准随机纳米结构在开发具有多种功能的先进材料体系中发挥着越来越重要的作用。这些结构不包括相同单元细胞的周期性重复，而是具有潜在空间相关性的看似随机的材料分布。与周期设计相比，准随机纳米结构通常需要昂贵和时间密集型的制造，可以通过低成本和可扩展的制造工艺来合成。然而，由于缺乏适当的计算设计范式，在微观结构设计和可行的纳米制造技术之间经常存在不匹配。虽然测试平台主要集中在有机光伏电池上，但这项研究将建立该方法在广泛的微结构系统中的适用性，其中感兴趣的特性/性能主要取决于空间相关性，而不是微观结构的局部几何形状。广泛的潜在工业和军事应用包括生物医疗设备、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、材料和力学等领域的研究人员提供了独特的合作体验。通过跨大学的教学和评估，以及在同一地点和共同指导的学生培训，研究将与教育相结合。并行设计方法创造了从现有的确定性计算材料工程到非确定性微观结构设计的转变，这种设计与自下而上的纳米制造工艺的内在随机性相兼容。关键的新颖之处在于，在工程材料系统的设计中，使用了物理感知谱密度函数（一种非确定性的微观结构表示）作为加工-结构和结构-性能映射之间的联系。该方法有助于快速探索可行和兼容的加工和结构解决方案，并显著降低了设计维度。利用粗粒度分子动力学实现原子分辨率的高性能计算探索，以了解基于材料加工依赖的结构形态演变的基本传输机制。自下而上的制造工艺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一个平台。最后，除了准随机纳米结构固有的鲁棒性外，该方法还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.