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We calculate bubble nucleation rates in a Lennard-Jones fluid through explicit molecular dynamics simulations. Our approach -- based on a recent free energy method (dubbed reweighted Jarzynski sampling), transition state theory, and a simple recrossing correction -- allows us to probe a fairly wide range of rates in several superheated and cavitation regimes in a consistent manner. Rate predictions from this approach bridge disparate independent literature studies on the same model system. As such, we find that rate predictions based on classical nucleation theory, direct brute force molecular dynamics simulations, and seeding are consistent with our approach and one another. Published rates derived from forward flux sampling simulations are, however, found to be outliers. This study serves two purposes. First, we validate the reliability of common modeling techniques and extrapolation approaches on a paradigmatic problem in materials science and chemical physics. Second, we further test our highly generic recipe for rate calculations, and establish its applicability to nucleation processes.

We introduce a simple enhanced sampling approach for the calculation of free energy differences and barriers along a one-dimensional reaction coordinate. First, a small number of short nonequilibrium simulations are carried out along the reaction coordinate, and the Jarzynski equality is used to learn an approximate free energy surface from the nonequilibrium work distribution. This free energy estimate is represented in a compact form as an artificial neural network and used as an external bias potential to accelerate rare events in a subsequent molecular dynamics simulation. The final free energy estimate is then obtained by reweighting the equilibrium probability distribution of the reaction coordinate sampled under the influence of the external bias. We apply our reweighted Jarzynski sampling recipe to four processes of varying scales and complexities-spanning chemical reaction in the gas phase, pair association in solution, and droplet nucleation in supersaturated vapor. In all cases, we find reweighted Jarzynski sampling to be a very efficient strategy, resulting in rapid convergence of the free energy to high precision.

The rate, selectivity and efficiency of plasma-based conversion processes is strongly affected by nonequilibrium phenomena. High concentrations of vibrationally excited molecules are such a plasma-induced effect. It is frequently assumed that vibrationally excited molecules are important in plasma catalysis because their presence lowers the apparent activation energy of dissociative chemisorption reactions and thus increases the conversion rate. A detailed atomic-level understanding of vibrationally stimulated catalytic reactions in the context of plasma catalysis is however lacking. Here, we couple a recently developed statistical model of a plasma-induced vibrational nonequilibrium to molecular dynamics simulations, enhanced sampling methods, and machine learning techniques. We quantify the impact of a vibrational nonequilibrium on the dissociative chemisorption barrier of H2 and CH4 on nickel catalysts over a wide range of vibrational temperatures. We investigate the effect of surface structure and compare the role of different vibrational modes of methane in the dissociation process. For low vibrational temperatures, very high vibrational efficacies are found, and energy in bend vibrations appears to dominate the dissociation of methane. The relative impact of vibrational nonequilibrium is much higher on terrace sites than on surface steps. We then show how our simulations can help to interpret recent experimental results, and suggest new paths to a better understanding of plasma catalysis.

The evaluation of nucleation rates from molecular dynamics trajectories is hampered by the slow nucleation time scale and impact of finite size effects. Here, we show that accurate nucleation rates can be obtained in a very general fashion relying only on the free energy barrier, transition state theory (TST), and a simple dynamical correction for diffusive recrossing. In this setup, the time scale problem is overcome by using enhanced sampling methods, in casu metadynamics, whereas the impact of finite size effects can be naturally circumvented by reconstructing the free energy surface from an appropriate ensemble. Approximations from classical nucleation theory are avoided. We demonstrate the accuracy of the approach by calculating macroscopic rates of droplet nucleation from argon vapor, spanning sixteen orders of magnitude and in excellent agreement with literature results, all from simulations of very small (512 atom) systems.

Although significant insights have been obtained into chemical and physical properties that govern to the performance of catalysts in traditional thermal processes, the work on electro-, photo-, or plasma-catalytic approaches has been comparatively limited. The effect of (local) surface charges in these processes, while most likely a crucial factor of their activity, has not been well-characterized and is difficult to study in a consistent, isolated manner. Even theoretical calculations, which have traditionally allowed for the untangling of the atomic-level mechanisms underpinning the catalytic process, cannot be readily applied to this class of problems because of their inability to properly treat systems carrying a net charge. Here, we report on a new, generic, and practical approach to deal with charged semiperiodic systems in density functional calculations, which can be readily applied to problems across surface science. Using this method, we investigate the effect of a negative catalyst surface charge on CO$_2$ activation by supported M/Al$_2$O$_3$ (M = Ti, Ni, Cu) single atom catalysts. The presence of an excess electron dramatically improves the reductive power of the catalyst, strongly promoting the splitting of CO$_2$ to CO and oxygen. 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.