  Despite recent advances of data acquisition and algorithms development,
machine learning (ML) faces tremendous challenges to being adopted in practical
catalyst design, largely due to its limited generalizability and poor
explainability. Here, we develop a theory-infused neural network (TinNet)
approach that integrates deep learning algorithms with the well-established
d-band theory of chemisorption for reactivity prediction of transition-metal
surfaces. With adsorbed hydroxyl (*OH) as a representative descriptor species,
we demonstrate that the TinNet is on par with purely data-driven ML methods in
prediction performance, while being inherently interpretable. Incorporation of
scientific knowledge of physical interactions into learning from data sheds
further light on the nature of chemical bonding and opens up new avenues for ML
discovery of novel motifs with desired catalytic properties.
