Methane dissociation on Ni(111): A fifteen-dimensional potential energy surface using neural network method

Dissociative chemisorption of methane on Ni(111) using a chemically accurate fifteen dimensional potential energy surface

A new chemically accurate potential energy surface for the dissociative chemisorption of methane on the rigid Ni(111) surface.

Effects of Lattice Motion on Dissociative Chemisorption: Toward a Rigorous Comparison of Theory with Molecular Beam Experiments

The dissociative chemisorption of small molecules such as methane and water on metal surfaces is a key step in many important catalyzed reactions. However, it has only very recently become possible to directly compare theory with molecular beam studies of these reactions. For most experimental conditions, such a comparison requires accurate methods for introducing the effects of lattice motion into quantum reactive scattering calculations. We examine these methods and their recent application to methane and water dissociative chemisorption. New results are presented for CO₂ chemisorption and methane dissociation at step edges. The type of molecule-lattice coupling that leads to a strong variation in the dissociative sticking of methane with temperature is shown to occur for many polyatomic-metal systems. Improvements to these models are discussed. The ability to accurately compare theory with molecular beam experiments should lead to improved density functionals and consequently more accurate thermal rate constants for these important reactions.

Multiscale Quantum Mechanics/Molecular Mechanics Simulations with Neural Networks

Molecular dynamics simulation with multiscale quantum mechanics/molecular mechanics (QM/MM) methods is a very powerful tool for understanding the mechanism of chemical and biological processes in solution or enzymes. However, its computational cost can be too high for many biochemical systems because of the large number of ab initio QM calculations. Semiempirical QM/MM simulations have much higher efficiency. Its accuracy can be improved with a correction to reach the ab initio QM/MM level. The computational cost on the ab initio calculation for the correction determines the efficiency. In this paper we developed a neural network method for QM/MM calculation as an extension of the neural-network representation reported by Behler and Parrinello. With this approach, the potential energy of any configuration along the reaction path for a given QM/MM system can be predicted at the ab initio QM/MM level based on the semiempirical QM/MM simulations. We further applied this method to three reactions in water to calculate the free energy changes. The free-energy profile obtained from the semiempirical QM/MM simulation is corrected to the ab initio QM/MM level with the potential energies predicted with the constructed neural network. The results are in excellent accordance with the reference data that are obtained from the ab initio QM/MM molecular dynamics simulation or corrected with direct ab initio QM/MM potential energies. Compared with the correction using direct ab initio QM/MM potential energies, our method shows a speed-up of 1 or 2 orders of magnitude. It demonstrates that the neural network method combined with the semiempirical QM/MM calculation can be an efficient and reliable strategy for chemical reaction simulations.

Chemically Accurate Simulation of a Polyatomic Molecule-Metal Surface Reaction

Although important to heterogeneous catalysis, the ability to accurately model reactions of polyatomic molecules with metal surfaces has not kept pace with developments in gas phase dynamics. Partnering the specific reaction parameter (SRP) approach to density functional theory with ab initio molecular dynamics (AIMD) extends our ability to model reactions with metals with quantitative accuracy from only the lightest reactant, H2, to essentially all molecules. This is demonstrated with AIMD calculations on CHD3 + Ni(111) in which the SRP functional is fitted to supersonic beam experiments, and validated by showing that AIMD with the resulting functional reproduces initial-state selected sticking measurements with chemical accuracy (4.2 kJ/mol ≈ 1 kcal/mol). The need for only semilocal exchange makes our scheme computationally tractable for dissociation on transition metals.

Rotational effects on the dissociation dynamics of CHD3 on Pt(111)

MCTDH calculations suggest that experiments on the effect of alignment on dissociation of CH₄ on metal surfaces are affected by steering.