Design and performance analysis of multi-enzyme activity-doped nanozymes assisted by machine learning.

Traditional design approaches for nanozymes typically rely on empirical methods and trial-and-error, which hampers systematic optimization of their structure and performance, thus limiting the efficiency of developing innovative nanozymes. This study leverages machine learning techniques supported by high-throughput computations to effectively design nanozymes with multi-enzyme activities and to elucidate their reaction mechanisms. Additionally, it investigates the impact of dopants on the microphysical properties of nanozymes. We constructed a machine learning prediction framework tailored for dopant nanozymes exhibiting catalytic activities like to oxidase (OXD) and peroxidase (POD). This framework was used to evaluate key catalytic performance parameters, such as formation energy, density of states (DOS), and adsorption energy, through density functional theory (DFT) calculations. Various machine learning models were employed to predict the effects of different doping element ratios on the catalytic activity and stability of nanozymes. The results indicate that the combination of machine learning with high-throughput computations significantly accelerates the design and optimization of dopant nanozymes, providing an efficient strategy to address the complexities of nanozyme design. This approach not only boosts the efficiency and capability for innovation in material design but also provides a novel theoretical analytical avenue for the development of new functional materials.