Microstructure design using graphs

Abstract

Thin films with tailored microstructures are an emerging class of materials with applications such as battery electrodes, organic electronics, and biosensors. Such thin film devices typically exhibit a multi-phase microstructure that is confined, and show large anisotropy. Current approaches to microstructure design focus on optimizing bulk properties, by tuning features that are statistically averaged over a representative volume. Here, we report a tool for morphogenesis posed as a graph-based optimization problem that evolves microstructures recognizing confinement and anisotropy constraints. We illustrate the approach by designing optimized morphologies for photovoltaic applications, and evolve an initial morphology into an optimized morphology exhibiting substantially improved short circuit current (68% improvement over a conventional bulk-heterojunction morphology). We show optimized morphologies across a range of thicknesses exhibiting self-similar behavior. Results suggest that thicker films (250 nm) can be used to harvest more incident energy. Our graph based morphogenesis is broadly applicable to microstructure-sensitive design of batteries, biosensors and related applications.

Interpretable deep learning for guided microstructure-property explorations in photovoltaics

“…6 where the evolution of the morphology is towards features with multiple scales, mimicking the finger-like fractal structures that are exhibited by high-performance morphologies. 40 We perform fullphysics simulations on one of the optimized morphologies (Fig. 6c), which confirms that the surrogate-derived morphology is in fact a high-performing morphology (Fig.…”

“…First, we can see how performance can be improved from a simple bilayer by increasing the amount of surface area between the acceptor and donor. 44 The maximum boost of performance is obtained when the donor(black) domains are fractal-like, 40 as shown in Fig. 5e.…”

“…In a more difficult generalizability test, we use fractal-like morphologies, 40 that are constructed to maximize the interfacial area while minimizing the amount of tortuous transport. These 'virtual' morphologies have been shown to exhibit enhanced performance, 40 but are currently difficult to experimentally fabricate. We make this point to emphasize that our training dataset consists fully of morphologies that are experimentally feasible to fabricate.…”

“…Figure 3 shows the saliency maps for morphologies in the data, columnar structures and the "high" performing morphologies identified in. 40 We can see, in Fig. 3, how the network uses the interface between the acceptor and donor regions feature as a key measure for prediction.…”

“…Notice how several physics based intuitive trends can be identified and understood by incrementally perturbing the original bilayer morphology approach to perturb morphologies and evolve them towards higher performance. 40 PBIL estimates the explicit probability distribution of the optimal morphology. The multi variate probability distribution is stored as a probability matrix P of the 2D morphology, i.e., each pixel is associated with a probability and is updated at each iteration to evolve towards promising morphology classes.…”