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In this paper, we leverage predictive uncertainty of deep neural networks toanswer challenging questions material scientists usually encounter in machinelearning based materials applications workflows. First, we show that byleveraging predictive uncertainty, a user can determine the required trainingdata set size necessary to achieve a certain classification accuracy. Next, wepropose uncertainty guided decision referral to detect and refrain from makingdecisions on confusing samples. Finally, we show that predictive uncertaintycan also be used to detect out-of-distribution test samples. We find that thisscheme 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 synthesisconditions. Using microstructure information from scanning electron microscope(SEM) images as an example use case, we show that leveraging uncertainty-awaredeep learning can significantly improve the performance and dependability ofclassification models.

Leveraging Uncertainty from Deep Learning for Trustworthy Materials Discovery Workflows