Interpretable deep learning for guided structure-property explorations in photovoltaics
This work addresses the challenge of designing efficient photovoltaics for researchers and engineers, but it is incremental as it applies existing deep learning methods to a specific domain problem.
The authors tackled the expensive evaluation of organic photovoltaic device performance by introducing a deep convolutional neural network as a fast surrogate model, enabling robust microstructural design to enhance performance.
The performance of an organic photovoltaic device is intricately connected to its active layer morphology. This connection between the active layer and device performance is very expensive to evaluate, either experimentally or computationally. Hence, designing morphologies to achieve higher performances is non-trivial and often intractable. To solve this, we first introduce a deep convolutional neural network (CNN) architecture that can serve as a fast and robust surrogate for the complex structure-property map. Several tests were performed to gain trust in this trained model. Then, we utilize this fast framework to perform robust microstructural design to enhance device performance.