Leveraging Transfer Learning and Chemical Principles toward Interpretable Materials Properties

Machine learning is emerging as a new paradigm to rationalize chemical properties for deepening our understanding of chemistry and providing instructive clues on better materials performance. While the complex architecture of machine learning contributes to unprecedented capability in this task, it prevents easy interpretation, leading to extensive criticisms on the lack of physical foundations for the black-box like models. Here, we demonstrate...