The hyperdynamics method is a powerful tool to simulate slow processes at the atomic level. However, the construction of an optimal hyperdynamics potential is a task that is far from trivial. Here, we propose a generally applicable implementation of the hyperdynamics algorithm, borrowing two concepts from metadynamics. First, the use of a collective variable (CV) to represent the accelerated dynamics gives the method a very large flexibility and simplicity. Second, a metadynamics procedure can be used to construct a suitable history-dependent bias potential on-the-fly, effectively turning the algorithm into a self-learning accelerated molecular dynamics method. This collective variable-driven hyperdynamics (CVHD) method has a modular design: both the local system properties on which the bias is based, as well as the characteristics of the biasing method itself, can be chosen to match the needs of the considered system. As a result, system-specific details are abstracted from the biasing algorithm itself, making it extremely versatile and transparent. The method is tested on three model systems: diffusion on the Cu(001) surface and nickel-catalyzed methane decomposition, as examples of “reactive” processes with a bond-length-based CV, and the folding of a long polymer-like chain, using a set of dihedral angles as a CV. Boost factors up to 109, corresponding to a time scale of seconds, could be obtained while still accurately reproducing correct dynamics.
Understanding the nature and effect of the multitude of plasma-surface interactions in plasma catalysis is a crucial requirement for further process development and improvement. A particularly intriguing and rather unique property of a plasma-catalytic setup is the ability of the plasma to modify the electronic structure, and hence chemical properties, of the catalyst through charging, i.e., the absorption of excess electrons. In this work, we develop a quantum chemical model based on density functional theory (DFT) to study excess negative surface charges in a heterogeneous catalyst exposed to a plasma. This method is specifically applied to investigate plasma-catalytic CO2 activation on supported M/Al2O3 (M = Ti, Ni, Cu) single atom catalysts. We find that (1) the presence of a negative surface charge dramatically improves the reductive power of the catalyst, strongly promoting the splitting of CO2 to CO and oxygen, and (2) the relative activity of the investigated transition metals is also changed upon charging, suggesting that controlled surface charging is a powerful additional parameter to tune catalyst activity and selectivity. These results strongly point to plasma-induced surface charging of the catalyst as an important factor contributing to the plasma-catalyst synergistic effects frequently reported for plasma catalysis.
Atomistic simulation methods for the quantification of free energies are in wide use. These methods operate by sampling the probability density of a system along a small set of suitable collective variables (CVs), which is in turn expressed in the form of a free energy surface (FES). This definition of the FES can capture the relative stability of metastable states, but not that of the transition state because the barrier height is not invariant to the choice of CVs. Free energy barriers therefore cannot be consistently computed from the FES. Here, we present a simple approach to calculate the gauge correction necessary to eliminate this inconsistency. Using our procedure, the standard FES as well as its gauge-corrected counterpart can be obtained by reweighing the same simulated trajectory at little additional cost. We apply the method to a number of systems-a particle solvated in a Lennard-Jones fluid, a Diels-Alder reaction, and crystallization of liquid sodium-to demonstrate its ability to produce consistent free energy barriers that correctly capture the kinetics of chemical or physical transformations, and discuss the additional demands it puts on the chosen CVs. Because the FES can be converged at relatively short (sub-ns) time scales, a free energy-based description of reaction kinetics is a particularly attractive option to study chemical processes at more expensive quantum mechanical levels of theory.
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