Enhanced Hydrogen Evolution Performance at the Lateral Interface between Two Layered Materials Predicted with Machine Learning.

While economical and effective catalysts are required for sustainable hydrogen production, low-dimensional interfacial engineering techniques have been developed to improve the catalytic activity in the hydrogen evolution reaction (HER). In this study, we used density functional theory (DFT) calculations to measure the Gibbs free energy change (Δ G H ) in hydrogen adsorption in two-dimensional lateral heterostructures (LHSs) MX 2 /M'X' 2 (MoS 2 /WS 2 , MoS 2 /WSe 2 , MoSe 2 /WS 2 , MoSe 2 /WSe 2 , MoTe 2 /WSe 2 , MoTe 2 /WTe 2 , and WS 2 /WSe 2 ) and MX 2 /M'X' (NbS 2 /ZnO, NbSe 2 /ZnO, NbS 2 /GaN, MoS 2 /ZnO, MoSe 2 /ZnO, MoS 2 /AlN, MoS 2 /GaN, and MoSe 2 /GaN) at several different positions near the interface. Compared to the interfaces of LHS MX 2 /M'X' 2 and the surfaces of the monolayer MX 2 and MX, the interfaces of LHS MX 2 /M'X' display greater hydrogen evolution reactivity due to their metallic behavior. The hydrogen absorption is stronger at the interfaces of LHS MX 2 /M'X', and that facilitates proton accessibility and increases the usage of catalytically active sites. Here, we develop three types of descriptors that can be used universally in 2D materials and can explain changes in Δ G H for different adsorption sites in a single LHS using only the basic information of the LHSs (type and number of neighboring atoms to the adsorption points). Using the DFT results of the LHSs and the various experimental data of atomic information, we trained machine learning (ML) models with the chosen descriptors to predict promising combinations and adsorption sites for HER catalysts among the LHSs. Our ML model achieved an R 2 score of 0.951 (regression) and an F 1 score of 0.749 (classification). Furthermore, the developed surrogate model was implemented to predict the structures in the test set and was based on confirmation from the DFT calculations via Δ G H values. The LHS MoS 2 /ZnO is the best candidate for HER among 49 candidates considered using both DFT and ML models because it has a Δ G H of -0.02 eV on top of O at the interface position and requires only -171 mV of overpotential to obtain the standard current density (10 A/cm 2 ).