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

Overview This interdisciplinary proposal addresses the challenges in designing complex nanostructured material systems (NMS) to simultaneously achieve performance optimality and nanofabrication feasibility. The research objective is to create a novel concurrent design framework that co-designs quasi-random nano/microstructures (a.k.a. microstructures) and bottom-up nanomanufacturing processes to accelerate the development of high-performance, cost effective, and robust NMS. Through the synergy of experts in computational materials design, materials characterization and nanofabrication, and mechanics, a collaborative microstructure and processing design architecture rooted in “microstructure mediated design” and the stochastics representation using the spectral density function (SDF) is proposed. The hybrid computational-experimental approach integrates diverse data from molecular computations, microstructure characterization, and device performance validation. With a long-term vision of solving challenges in energy sustainability, organic photovoltaic cells (OPVC) is used as a test bed to address the research issues in quasi-random NMS design and validate the proposed methodology. Intellectual Merit The proposed systematic design approach creates a shift from existing deterministic computational materials engineering to non-deterministic microstructure design that is compatible with the intrinsic stochasticity of nanomanufacturing processes. The key novelty is to use the physics-aware SDF, a non-deterministic microstructure representation, as the link between the processing-structure and the structure-performance mappings in design of engineered material systems. Beyond existing approaches, SDFs can model complex morphologies with arbitrary microstructure geometries and the key characteristics in SDF have direct associations with functional performance and formation processes. Therefore the approach facilitates a quick exploration of feasible and compatible processing and structure solutions, with a significantly reduced dimensionality in design search. The use of coarse-grained molecular dynamics (CGMD) simulations to understand the processing dependent evolution of structural morphologies will make significant strides in developing a robust predictive methodology to establish processing-structure-performance relationships. The bottom-up fabrication processes and imaging techniques created for OPVC offer a platform for validation of the SDF approach, calibration and validation of computational findings, and deep scientific discovery. Finally, in addition to the intrinsic robustness of quasi-random nanostructures, our approach offers robust NMS designs considering not only the variations in processing conditions but also the uncertainty of model itself. Broader Impact This research addresses several top scientific challenges in computational design of engineered material systems, namely (1) microstructure representation applicable for a wide range of material systems, (2) seamless integration of processing-structure-property-performance evaluations, (3) dimension reduction in material design synthesis, and (4) material design considering processing feasibility. Although the test bed is focused on OPVC devices, the proposed research will establish the applicability of the SDF approach for designing a wide range of microstructural systems where the properties/performance of interest mainly depend on the spatial correlations instead of local geometries of microstructures. The broad range of industrial and military applicati