Explainable Deep Learning for Uncovering Actionable Scientific Insights for Materials Discovery and Design
It addresses the challenge of interpretability for scientists in materials discovery and design, though it appears incremental as it builds on existing generative modeling frameworks.
The paper tackles the problem of extracting actionable knowledge from opaque deep neural networks in materials science by proposing techniques that inject domain-specific attributes as tunable knobs in the analysis pipeline, enabling better model understanding and actionable insights for scientists.
The scientific community has been increasingly interested in harnessing the power of deep learning to solve various domain challenges. However, despite the effectiveness in building predictive models, fundamental challenges exist in extracting actionable knowledge from deep neural networks due to their opaque nature. In this work, we propose techniques for exploring the behavior of deep learning models by injecting domain-specific actionable attributes as tunable "knobs" in the analysis pipeline. By incorporating the domain knowledge in a generative modeling framework, we are not only able to better understand the behavior of these black-box models, but also provide scientists with actionable insights that can potentially lead to fundamental discoveries.