Best-of-Both-Worlds Predictive Approach to Dissociative Chemisorption on Metals.

Predictive capability, accuracy, and affordability are essential features of a theory that is capable of describing dissociative chemisorption on a metal surface. This type of reaction is important for heterogeneous catalysis. Here we present an approach in which we use diffusion Monte Carlo (DMC) to pin the minimum barrier height and construct a density functional that reproduces this value.

Assessing the dynamics of CO adsorption on Cu(110) using the vdW-DF2 functional and artificial neural networks.

In this work, we revisit the dynamics of carbon monoxide molecular chemisorption on Cu(110) by using quasi-classical trajectory calculations. The molecule-surface interaction is described through an atomistic neural network approach based on Density Functional Theory calculations using a nonlocal exchange-correlation (XC) functional that includes the effect of long-range dispersion forces: vdW-DF2 [Lee et al. Phys.

Simulating Highly Activated Sticking of H on Al(110): Quantum versus Quasi-Classical Dynamics.

We evaluate the importance of quantum effects on the sticking of H on Al(110) for conditions that are close to those of molecular beam experiments that have been done on this system. Calculations with the quasi-classical trajectory (QCT) method and with quantum dynamics (QD) are performed using a model in which only motion in the six molecular degrees of freedom is allowed. The potential energy surface used has a minimum barrier height close to the value recently obtained with the quantum Monte Carlo method.

Methylidyne Adsorption on Pt(211) Probed by Reflection Absorption Infrared Spectroscopy (RAIRS).

Methylidyne, CH(ads), adsorbed on a Pt(211) surface and its interaction with chemisorbed hydrogen atoms was studied by reflection absorption infrared spectroscopy (RAIRS). Methylidyne was formed on Pt(211) by methane dissociation from a molecular beam followed by thermal decomposition of the methane dissociation products. CH(ads) was detected by RAIRS via its symmetric C-H stretch vibration resulting in three discrete absorption peaks in the region of 2950-2970 cm.

High-Dimensional Atomistic Neural Network Potential to Study the Alignment-Resolved O Scattering from Highly Oriented Pyrolytic Graphite.

A high dimensional and accurate atomistic neural network potential energy surface (ANN-PES) that describes the interaction between one O molecule and a highly oriented pyrolytic graphite (HOPG) surface has been constructed using the open-source package (aenet). The validation of the PES is performed by paying attention to static characteristics as well as by testing its performance in reproducing previous ab initio molecular dynamics simulation results. Subsequently, the ANN-PES is used to perform quasi-classical molecular dynamics calculations of the alignment-dependent scattering of O from HOPG.