Abstract
Molecular and materials science are central to addressing global challenges in healthcare, energy sustainability, environmental protection, and nextgeneration technologies. Applications such as drug discovery, energy storage, carbon capture, catalyst design, and semiconductor development highlight the transformative potential of these fields. At the core of these advances is the ability to design and analyze complex molecular and material systems.AI for scientific discovery has therefore attracted growing interest across machine learning, physics, chemistry, and materials science. A key challenge is building effective and efficient models of molecules and materials.
Although deep learning can capture complex chemical and physical behavior, its “blackbox” nature often limits its ability to yield actionable scientific insights.This presentation underscores the essential role of interpretability in deep learning. By enhancing trust in model predictions and enabling the extraction of meaningful mechanistic understanding, interpretable AI frameworks empower scientists to uncover new principles and accelerate systematic discovery.