| Abstract Scope |
The formation and breakage of chemical bonds at active sites is the molecular basis of catalysis. Being able to rapidly compute interaction strengths between bonding entities and understand their trends holds the key to the design of improved catalysts. Despite recent advances, machine learning (ML) faces a tremendous challenge for catalysis applications due to its poor transferability and explainability. Here we present theory-infused machine learning (TIML) algorithms that integrates convolutional neural
networks with the d-band theory of chemisorption for predicting the chemical reactivity of metal surfaces. With *OH and *CO as two representative adsorbates, we demonstrated that the hybrid ML models outperform the purely data-driven ones in both data scarce and rich regions, especially for out-of-sample systems. More importantly, the architecture design enables its physical interpretability, shedding light on the nature of chemical
bonding at metal surfaces. |