Neural Network-Assisted Development of High-Entropy Alloy Catalysts: Decoupling Ligand and Coordination Effects

High-entropy alloys (HEAs) recently emerged as promising catalysts due to their immense chemical space and tunability. However, the large chemical space presents challenges for comprehensive characterization due to experiments' trial-and-error nature. Here, we leverage neural network (NN) and density functional theory to simultaneously account for ligand effect (spatial arrangement of different elements) and coordination effect ( different crystal facets and defects) for predicting the adsorption energy. The developed NN model demonstrates three advantages: (1) high accuracy, with a mean absolute error of 0.09 eV; (2) universality, with applicability to bimetallic catalysts; and (3) simplicity, with 36 NN parameters and its further simplification to a linear scaling model at a slight loss of accuracy. Using the trained NN model validated with experimental literature, we decouple the comparative extents of ligand and coordination effects. Our results endow high practical significance and provide important insights for rational design of HEA catalysts.