Machine learning guided O2 reduction to H2O2 on BN co-doped γ-graphyne: insights from time-domain atomistic dynamics.

Visible-light-driven production of hydrogen peroxide (H2O2) offers a promising and sustainable pathway. Metal-free carbon-based materials are attractive photocatalysts for the two-electron oxygen reduction reaction (2e- ORR), but their performance is often limited by rapid electron-hole recombination and sluggish kinetics. To overcome these limitations, we have performed combined density functional theory (DFT) and nonadiabatic molecular dynamics (NAMD) simulations to investigate the photocatalytic conversion of O2 to H2O2 on a BN co-doped γ-graphyne monolayer. Upon BN co-doping, the band gap of pristine γ-graphyne increases from 0.96 to 1.10 eV, while retaining favorable band edge positions and strong UV-Vis absorption. We herein employed pretrained Universal Model for Atoms (UMA) to identify the most favorable adsorption site on the BN co-doped γ-graphyne monolayer. Gibbs free energy calculations confirm the thermodynamic feasibility of the reaction under light irradiation. Importantly, the NAMD results reveal a significantly prolonged electron-hole recombination time (10.75 ns) as compared to the pristine system (4.17 ns), providing sufficient time for photogenerated electrons to effectively participate in the reduction of O2 to H2O2. Overall, our results suggest that BN co-doped γ-graphyne can serve as a promising and efficient metal-free photocatalyst for converting O2 to H2O2 under UV-vis light.