Energy parameter and electronic descriptor for carbon based catalyst predicted using QM/ML

Descriptor based model can be efficient in identifying an optimal carbon-based catalyst for oxygen evolution reaction (OER). Here, we correlate the O-atom adsorption strength with the OER activity of graphene nanoribbon systems and define the energy parameters (Delta G(O)-Delta G(OH)) to identify the overpotential (eta). The pi electron based descriptor can predict the catalytic activity of the graphene surfaces. Machine learning algorithms like Multiple Linear Regression, Random Forest Regression and Support Vector Regression (SVR) are trained on the data generated by density functional theory to predict the overpotential. An optimal active site for OER using proposed SVR model is identified with overpotential (0.29 V) and then validate through DFT calculations. To generalize the study, we used SVR model on N doped GNR to predict the site-specific activity towards OER. Such a combined approach can be extended to estimate the site-specific OER activity of different carbon catalysts at a dramatically reduced computational cost.

Automated summary

The study utilized a descriptor-based model to predict the activity of carbon-based catalysts for OER and trained machine learning algorithms to predict overpotential. The proposed method can estimate site-specific OER activity of different carbon catalysts at a lower computational cost.