High-throughput screening of bimetallic catalysts enabled by machine learning

We present a holistic machine-learning framework for rapid screening of bimetallic catalysts with the aid of the descriptor-based kinetic analysis. A catalyst database, which contains the adsorption energies of *CO and *OH on {111}-terminated model alloy surfaces and fingerprint features of active sites from density functional theory calculations with the semi-local generalized gradient approximation (GGA), is established and used in optimizing the structural and weight parameters of artificial neural networks. The fingerprint descriptors, rooted at the d-band chemisorption theory and its recent developments, include the sp-band and d-band characteristics of an adsorption site together with tabulated properties of host-metal atoms. Using methanol electro-oxidation as the model reaction, the machine-learning model trained with the existing dataset of ∼1000 idealized alloy surfaces can capture complex, non-linear adsorbate/metal interactions with the RMSE ∼ 0.2 eV and shows predictive power in exploring the immense chemical space of bimetallic catalysts. Feature importance analysis sheds light on the underlying factors that govern the adsorbate/metal interactions and provides the physical origin of bimetallics in breaking energy-scaling constraints of *CO and *OH, the two most commonly used reactivity descriptors in heterogeneous catalysis. We present a holistic machine-learning framework for rapid screening of bimetallic catalysts with the aid of the descriptor-based kinetic analysis.