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. We find that random forests provide accurate predictions and that the best features are combinations of traditional chemical and structural descriptors. Further analysis of our model errors and the underlying forest kernel reveals unexpected finite-size electronic structure effects: embedded dopant atoms can display counterintuitive behavior such as nonmonotonic trends as a function of composition and high sensitivity to dopants far from the adsorbing H atom. We explain these behaviors with simple tight-binding Hamiltonians and d-orbital densities of states. We also use variations among forest leaves to predict the uncertainty of predictions, which allows us to mitigate the effects of larger errors.

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. The dynamic interaction of water with adsorbed O maintains a high dispersion of O on the surface, potentially creating reactive transient species. At low temperature and pressure, isotopic experiments show that adsorbed oxygen on the Au(110) surface exchanges with oxygen in H218O. The presence of water modulates local electronic properties and facilitates oxygen exchange. Combining experimental results and theory, we propose that hydroxyl is transiently formed via proton transfer from the water to adsorbed oxygen. Hydroxyl groups easily recombine to regenerate water and adsorbed oxygen atoms, the net result of which is migration of the adsorbed oxygen without significant change in its overall distribution on the surface. The presence of water creates a dynamic surface where mobile surface oxygen atoms and hydroxyls are present, which can lead to a better performance of gold catalysis in oxidation reactions.

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. We propose a strategy for employing bending deformations in bilayer TMDCs as a simple yet effective means of dynamically and reversibly tuning their band gaps while simultaneously tuning valley-selective physics.