The microstructure determines the photovoltaic performance of a thin film organic semiconductor film. The relationship between microstructure and performance is usually highly non-linear and expensive to evaluate, thus making microstructure optimization challenging. Here, we show a data-driven approach for mapping the microstructure to photovoltaic performance using deep convolutional neural networks. We characterize this approach in terms of two critical metrics, its generalizability (has it learnt a reasonable map?), and its intepretability (can it produce meaningful microstructure characteristics that influence its prediction?). A surrogate model that exhibits these two features of generalizability and intepretability is particularly useful for subsequent design exploration. We illustrate this by using the surrogate model for both manual exploration (that verifies known domain insight) as well as automated microstructure optimization. We envision such approaches to be widely applicable to a wide variety of microstructure-sensitive design problems.
The microstructure of a material intimately affects the performance of a device made from this material. The microstructure, in turn, is affected by the processing pathway used to fabricate the device. This forms the process-structure-property triangle that is central to material science. There has been increasing interest to comprehensively understand and subsequently exploit process-structure-property (PSP) relationships to design processing pathways that result in tailored microstructures exhibiting optimal properties. However, unraveling process-structure-property relationships usually requires systematic and tedious combinatorial search of process and system variables to identify the microstructures that are produced. This is further complicated by the necessity to interrogate the properties of the huge set of corresponding microstructures. Motivated by this challenge, we focus on developing a generic methodology to establish and explore PSP pathways. We leverage recent advances in high performance computing (HPC) and high throughput computing (HTC) with the premise that a domain expert should be able to focus on domain specific PSP problems while the highly specialized HPC/HTC knowledge needed to approach such problems should be hidden from the domain expert. Our hypothesis is that PSP exploration can be naturally formulated in terms of a standard paradigm in Cloud Computing, namely the MapReduce programming model. We show how reformulating PSP exploration into a MapReduce workflow enables us to take advantage of advances in cloud computing while requiring minimal specialized knowledge of HPC. We illustrate this generic approach by exploring PSP relationships relevant to organic photovoltaics. We focus on identifying microstructural traits that correlate with specific properties of the photovoltaic process: exciton generation, exciton dissociation and charge generation. We integrate a graph-based microstructure characterization tool, and a microstructure-aware device simulator into the MapReduce workflow to automatically generate, explore and identify highly correlated microstructural traits. Identification of these microstructural traits has significant implications for designing the next generation of organic photovoltaics.
Recent advances in the efficiency of organic photovoltaics (OPVs) have been driven by judicious selection of processing conditions that result in a 'desired' morphology. An important theme of morphology research is the need to quantify the effect of processing conditions on nanoscale morphology and to relate this information to device efficiency in a closed loop. However, accurate and comprehensive morphology quantification still remains a pressing challenge. State-of-the-art quantification methods like XRD, GIWAXS, GISAXS and TEM provide film averaged or 2D projected features that only indirectly correlate with performance, thus making causal reasoning nontrivial. Accessing the 3D distribution of material however provides a natural means of directly mapping processing conditions to device performance. In this paper, we integrate two recently developed techniquesreconstruction of 3D spatial maps of morphology (HAADF-STEM and DART) and conversion of the resulting 3D maps into intuitive morphology descriptors (GraSPI)to comprehensively image and quantify 3D morphology under various processing conditions. We apply these techniques on films generated by two of the most common fabrication techniques, doctor blading and spin coating, additionally investigating the impact of thermal annealing on these samples. We find that the morphology of all samples exhibit very high connectivity to the electrodes. Not surprisingly, thermal annealing consistently increases the average domain size of polymer/fullerene rich phases in the samples, aiding improved exciton generation. Furthermore, annealing also improves the balance of interfaces, aiding enhanced exciton dissociation. The spinannealed sample was observed to have a very balanced distribution of charge transport paths to both electrodes, aiding enhanced charge transport and collection. A comparison of morphology descriptors impacting each stage of the photo physics (exciton generation, exciton dissociation, charge transport) reveals that the spin-annealed sample exhibits superior morphology-based performance indicators. This suggests that there is substantial room for improvement of blade based methods in terms of process optimization for morphology tuning to achieve enhanced performance of large area devices.
Accelerating the development of π-conjugated molecules for applications such as energy generation and storage, catalysis, sensing, pharmaceuticals, and (semi)conducting technologies requires rapid and accurate evaluation of the electronic, redox, or...
Generative Adversarial Networks (GANs), while widely successful in modeling complex data distributions, have not yet been sufficiently leveraged in scientific computing and design. Reasons for this include the lack of flexibility of GANs to represent discrete-valued image data, as well as the lack of control over physical properties of generated samples. We propose a new conditional generative modeling approach (InvNet) that efficiently enables modeling discrete-valued images, while allowing control over their parameterized geometric and statistical properties. We evaluate our approach on several synthetic and real world problems: navigating manifolds of geometric shapes with desired sizes; generation of binary two-phase materials; and the (challenging) problem of generating multi-orientation polycrystalline microstructures.
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