Machine Learning Prediction of H Adsorption Energies on Ag Alloys.

Adsorption energies on surfaces are excellent descriptors of their chemical properties, including their catalytic performance. High-throughput adsorption energy predictions can therefore help accelerate first-principles catalyst design. To this end, we present over 5000 DFT calculations of H adsorption energies on dilute Ag alloys and describe a general machine learning approach to rapidly predict H adsorption energies for new Ag alloy structures.

Water facilitates oxygen migration on gold surfaces.

The water-oxygen-gold interface is important in many surface processes and has potential influence on heterogeneous catalysis. Herein, it is shown that water facilitates the migration of atomic oxygen on Au(110), demonstrating the dynamic nature of surface adsorption. We demonstrate this effect for the first time, using in situ scanning tunnelling microscopy (STM), temperature-programmed reaction spectroscopy (TPRS) and first-principles theoretical calculations.

Density-functional tight-binding simulations of curvature-controlled layer decoupling and band-gap tuning in bilayer MoS2.

Monolayer transition-metal dichalcogenides (TMDCs) display valley-selective circular dichroism due to the presence of time-reversal symmetry and the absence of inversion symmetry, making them promising candidates for valleytronics. In contrast, in bilayer TMDCs both symmetries are present and these desirable valley-selective properties are lost. Here, by using density-functional tight-binding electronic structure simulations and revised periodic boundary conditions, we show that bending of bilayer MoS2 sheets breaks band degeneracies and localizes states on separate layers due to bending-induced strain gradients across the sheets.