  In this paper, we leverage predictive uncertainty of deep neural networks to
answer challenging questions material scientists usually encounter in machine
learning based materials applications workflows. First, we show that by
leveraging predictive uncertainty, a user can determine the required training
data set size necessary to achieve a certain classification accuracy. Next, we
propose uncertainty guided decision referral to detect and refrain from making
decisions on confusing samples. Finally, we show that predictive uncertainty
can also be used to detect out-of-distribution test samples. We find that this
scheme is accurate enough to detect a wide range of real-world shifts in data,
e.g., changes in the image acquisition conditions or changes in the synthesis
conditions. Using microstructure information from scanning electron microscope
(SEM) images as an example use case, we show that leveraging uncertainty-aware
deep learning can significantly improve the performance and dependability of
classification models.
