Machine Learning Assisted Graphdiyne-Based Nanozyme Discovery

Improving the catalytic activity and broadening the scope of nanozymes are prerequisites to supplement or even supersede natural enzymes. However, the discovery of nanozymes is mostly relied on serendipity with limited fine-tunings of chemical composition, which is often incomprehensive and fragmented. Machine learning (ML) is a promising solution to predict the nanozyme performance and thus accelerate the nanozyme development. Herein, a thorough investigation of the peroxidase (POD) mimic reaction catalyzed by nonmetal atom doped graphdiyne (GDY) is presented and two doped GDYs (B-GDY and N-GDY) with best performance were screened out. Specifically, the extreme gradient boosting (XGB) algorithm can mine the connection between the model parameters and maximum energy barrier (R2 > 78%) or maximum energy consuming step (accuracy > 65%) from the data set constructed by all the nonmetal atom doped GDYs, which provides a method to effectively reduce 20% calculations. In addition, six nonmetal atom doped GDYs with different expected properties were synthesized, and their activity trends in three related experiments were consistent with the predicted results. This study demonstrates that ML can be a useful tool to assist density functional theory (DFT) computational screening and can serve as a guide for optimizing nanozyme performance, depending on doping strategy.