In this work, high-performance two-dimensional (2D) graphene-based single-atom electrocatalysts (ZZ/ZA-MN x C y ) for the oxygen reduction reaction (ORR) were screened out using machine learning (ML). A model was built for the fast prediction of electrocatalysts and two descriptors valence electron correction (VEc) and degree of construction differences (DC) were proposed to improve the accuracy of the model prediction. Two evaluation criteria, high-performance catalyst retention rate r R and high-performance catalyst occupancy rate r O , were proposed to evaluate the accuracy of ML models in high-performance catalyst screening. The addition of VEc and DC in the model could change the mean absolute error (MAE test ) of the test set, the coefficient of determination ( R 2 test ) of the test set, r O , and r R from 0.334 V, 0.683, 0.222, and 0.360 to 0.271 V, 0.774, 0.421, and 0.671, respectively. The partially screened potential high-performance ORR electrocatalysts such as ZZ-CoN 4 and ZZ-CoN 3 C 1 were also further investigated using a Density Functional Theory (DFT) method, which confirmed the accuracy of the ML model (MAE = 0.157 V, R 2 = 0.821).
Keyphrases
- density functional theory
- room temperature
- molecular dynamics
- ionic liquid
- dendritic cells
- reduced graphene oxide
- magnetic resonance imaging
- metal organic framework
- magnetic resonance
- gold nanoparticles
- electron transfer
- carbon dioxide
- molecular docking
- carbon nanotubes
- human health
- climate change
- liquid chromatography
- high speed
- single molecule
- molecularly imprinted
- clinical evaluation