(304f) Heterogenous Morphology By Design - Harnessing Cloud to Quantify and Optimize Morphology

Internal spatial organization of material is known to play a key role in engineered and natural heterogenous systems. Understanding interfacial phenomena and tailoring such heterogenous systems to obtain a desired functionality is a crucial element of energy-related research, advanced manufacturing, micro-fluidic technologies, as well as for personalized medicine.

We present the generic approach to screen large data sets of morphologies. Our approach is based on a MapReduce algorithm. Inherent parallelism of MapReduce and its support for cloud computing makes it a very attractive approach  to perform the efficient and fault tolerant accelerated analysis. We present how to harness it to establish process-structure-property link in efficient and generic way. We illustrate our approach by analyzing structure-property link of organic photovoltaics. In the mapping, stage we emit large set of representative morphologies along (value) with morphology descriptors (key)). We use a graph based strategy to extract physically meaningful morphology descriptors.  In reducing stage, we collect morphologies of the same key and perform full-scale analysis of its properties. This full scale characterization is accomplished using a drift-diffusion model that virtual interrogates the morphology to construct the J-V characteristics.

Subsequently, we use optimization techniques coupled with computational modeling to identify the optimal structures for high efficiency solar cells. In particular, we use adaptive population-based incremental learning method linked to graph-based surrogate model to evaluate properties for given structure.  We study several different criterions and find optimal structure that that improve the performance of currently hypothesized optimal structures by 29%.