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The scientific community has been increasingly interested in harnessing thepower of deep learning to solve various domain challenges. However, despite theeffectiveness in building predictive models, fundamental challenges exist inextracting actionable knowledge from deep neural networks due to their opaquenature. In this work, we propose techniques for exploring the behavior of deeplearning models by injecting domain-specific actionable attributes as tunable"knobs" in the analysis pipeline. By incorporating the domain knowledge in agenerative modeling framework, we are not only able to better understand thebehavior of these black-box models, but also provide scientists with actionableinsights that can potentially lead to fundamental discoveries.

Explainable Deep Learning for Uncovering Actionable Scientific Insights for Materials Discovery and Design