Bimetallic nanoparticles (NPs) with peroxidase-like (POD-like) activity play a crucial role in biosensing, disease treatment, environmental management, and other fields. However, their development is impeded by a vast range of tunable properties in components and structures, making the establishment of structure-effect relationships and the discovery of active materials challenging. Addressing this, we established robust scaling relationships by meticulously analyzing the catalytic reaction networks of pure metal NPs, which laid the volcano-shaped correlation between the activity and O* adsorption energy. Utilizing these relationships, we introduced an innovative and versatile descriptor of the NPs, which was then integrated into a machine learning-accelerated high-throughput computational workflow, significantly boosting the predictive accuracy for the POD-like activity of bimetallic NPs. Our methodological approach enabled the successful prediction of activities for 1260 bimetallic NPs, leading to the identification of several highly effective catalysts. Furthermore, we distilled several strategies for designing efficient bimetallic NPs based on our screening results.
Keywords: DFT calculations; bimetallic nanoparticles; heterogeneous catalysis; high-throughput screening; peroxidase mimics.