  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 simple adsorbates (e.g., *OH, *O, and *N) at active site
ensembles as 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.
