Dynamics and kinetics exploration of the oxygen reduction reaction at the Fe-N/C-water interface accelerated by a machine learning force field.

Understanding the oxygen reduction reaction (ORR) mechanism and accurately characterizing the reaction interface are essential for improving fuel cell efficiency. We developed an active learning framework combining machine learning force fields and enhanced sampling to explore the dynamics and kinetics of the ORR on Fe-N/C using a fully explicit solvent model. Different possible reaction paths have been explored and the O adsorption process is confirmed as the rate-determining step of the ORR at the Fe-N/C-water interface, which needs to overcome a free energy barrier of 0.

Interface engineering of heterogeneous transition metal chalcogenides for electrocatalytic hydrogen evolution.

MoS and MoSe are recognized as promising electrocatalysts for the hydrogen evolution reaction (HER), but the active sites are mainly located on the edge, limiting their electrochemical efficiency. Here we have introduced the 2H-1T' interface structures in MoSSe and MoS-MoSe heterostructures to enhance the HER activity in the basal planes by using the density functional theory (DFT) calculations. The structural stability and electronic properties of different 2H-1T' interface structures are investigated and the HER activities are evaluated by using the H adsorption free energy (Δ ).