Ab initio molecular dynamics (AIMD) simulations enable the accurate description of reactive molecule–surface scattering especially if energy transfer involving surface phonons is important. However, presently, the computational expense of AIMD rules out its application to systems where reaction probabilities are smaller than about 1%. Here we show that this problem can be overcome by a high-dimensional neural network fit of the molecule–surface interaction potential, which also incorporates the dependence on phonons by taking into account all degrees of freedom of the surface explicitly. As shown for N2 + Ru(0001), which is a prototypical case for highly activated dissociative chemisorption, the method allows an accurate description of the coupling of molecular and surface atom motion and accurately accounts for vibrational properties of the employed slab model of Ru(0001). The neural network potential allows reaction probabilities as low as 10–5 to be computed, showing good agreement with experimental results.

Electron–hole pair (ehp) excitation is thought to substantially affect the dynamics of molecules on metal surfaces, but it is not clear whether this can be better addressed by orbital-dependent friction (ODF) or the local density friction approximation (LDFA). We investigate the effect of ehp excitation on the dissociative chemisorption of N2 on and its inelastic scattering from Ru(0001), which is the benchmark system of highly activated dissociation, with these two different models. ODF is in better agreement with the best experimental estimates for the reaction probabilities than LDFA, yields results for vibrational excitation in better agreement with experiment, but slightly overestimates the translational energy loss during scattering. N2 on Ru(0001) is thus the first system for which the ODF and LDFA approaches are shown to yield substantially different results for easily accessible experimental observables, including reaction probabilities.

An accurate description of reactive scattering of molecules on metal surfaces often requires the modeling of energy transfer between the molecule and the surface phonons. Although ab initio molecular dynamics (AIMD) can describe this energy transfer, AIMD is at present untractable for reactions with reaction probabilities smaller than 1%. Here, we show that it is possible to use a neural network potential to describe a polyatomic molecule reacting on a mobile metal surface with considerably reduced computational effort compared to AIMD. The highly activated reaction of CHD3 on Cu(111) is used as a test case for this method. It is observed that the reaction probability is influenced considerably by dynamical effects such as the bobsled effect and surface recoil. A special dynamical effect for CHD3 + Cu(111) is that a higher vibrational efficacy is obtained for two quanta in the CH stretch mode than for a single quantum.

The electronic properties of a triaxially strained hexagonal graphene flake with either armchair or zig-zag edges are investigated using molecular dynamics simulations and tight-binding calculations. We found that: i) the pseudo-magnetic field in the strained graphene flakes is not uniform neither in the center nor at the edge of zig-zag terminated flakes, ii) the pseudo-magnetic field is almost zero in the center of armchair terminated flakes but increases dramatically near the edges, iii) the pseudo-magnetic field increases linearly with strain, for strains lower than 15% while growing nonlinearly beyond this threshold, iv) the local density of states in the center of the zig-zag hexagon exhibits pseudo-Landau levels with broken sub-lattice symmetry in the zero'th pseudo-Landau level, and in addition there is a shift in the Dirac cone due to strain induced scalar potentials. This study provides a realistic model of the electronic properties of inhomogeneously strained graphene where the relaxation of the atomic positions is correctly included together with strain induced modifications of the hopping terms up to next-nearest neighbors.

Direct electrical probing of molecular materials is often impaired by their insulating nature. Here, graphene is interfaced with single crystals of a molecular spin crossover complex, [Fe(bapbpy)(NCS)2], to electrically detect phase transitions in the molecular crystal through the variation of graphene resistance. Contactless sensing is achieved by separating the crystal from graphene with an insulating polymer spacer. Next to mechanical effects, which influence the conductivity of the graphene sheet but can be minimized by using a thicker spacer, a Dirac point shift in graphene is observed experimentally upon spin crossover. As confirmed by computational modeling, this Dirac point shift is due to the phase‐dependent electrostatic potential generated by the crystal inside the graphene sheet. This effect, named as chemo‐electric gating, suggests that molecular materials may serve as substrates for designing graphene‐based electronic devices. Chemo‐electric gating, thus, opens up new possibilities to electrically probe chemical and physical processes in molecular materials in a contactless fashion, from a large distance, which can enhance their use in technological applications, for example, as sensors.