Nucleation rates from small scale atomistic simulations and transition state theory

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

Quantifying the impact of vibrational nonequilibrium in plasma catalysis: Insights from a molecular dynamics model of dissociative chemisorption

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

Reactive plasma cleaning and restoration of transition metal dichalcogenide monolayers

The cleaning of two-dimensional (2D) materials is an essential step in the fabrication of future devices, leveraging their unique physical, optical, and chemical properties. Part of these emerging 2D materials are transition metal dichalcogenides (TMDs). So far there is limited understanding of the cleaning of “monolayer” TMD materials. In this study, we report on the use of downstream H2 plasma to clean the surface of monolayer WS2 grown by MOCVD. We demonstrate that high-temperature processing is essential, allowing to maximize the removal rate of polymers and to mitigate damage caused to the WS2 in the form of sulfur vacancies. We show that low temperature in situ carbonyl sulfide (OCS) soak is an efficient way to resulfurize the material, besides high-temperature H2S annealing. The cleaning processes and mechanisms elucidated in this work are tested on back-gated field-effect transistors, confirming that transport properties of WS2 devices can be maintained by the combination of H2 plasma cleaning and OCS restoration. The low-damage plasma cleaning based on H2 and OCS is very reproducible, fast (completed in a few minutes) and uses a 300 mm industrial plasma etch system qualified for standard semiconductor pilot production. This process is, therefore, expected to enable the industrial scale-up of 2D-based devices, co-integrated with silicon technology

Reweighted Jarzynski sampling: Acceleration of rare events and free energy calculation with a bias potential learned from nonequilibrium work

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

Merging Metadynamics into Hyperdynamics: Accelerated Molecular Simulations Reaching Time Scales from Microseconds to Seconds

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

Merging Metadynamics into Hyperdynamics: Accelerated Molecular Simulations Reaching Time Scales from Microseconds to Seconds

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

Activation of CO2 on Copper Surfaces: The Synergy Between Electric Field, Surface Morphology and Excess Electrons

In this work we use DFT calculations to study the combined effect of external electric fields, surface morphology and surface charge on CO2 activation over Cu (111), Cu (211), Cu (110) and Cu (001) surfaces. We observe that the binding energy of the CO2 molecule on Cu surfaces rises significantly upon increasing the applied electric field strength. In addition, rougher surfaces respond more effectively to the presence of the external electric field towards facilitating the formation of a carbonate-like CO2 structure and the transformation of the most stable adsorption mode from physisorption to chemisorption. The presence of surface charges further strengthens the electric field effect and consequently gives rise to an improved bending of the CO2 molecule and C-O bond length elongation. On the other hand, a net charge in the absence of externally applied electric field shows only a marginal effect on CO2 binding. The chemisorbed CO2 is more stable and further activated when the effects of an external electric field, rough surface and surface charge are combined. These results can help to elucidate the underlying factors that control CO2 activation in heterogeneous and plasma catalysis, as well as in electrochemical processes.</p

Open Concepts as Crystallization Points and Enablers of Discursive Configurations: The Case of the Innovation Campus in the Netherlands

Contains fulltext : 112139.pdf (publisher's version ) (Closed access) Contains fulltext : 112139-a.pdf (author's version ) (Open Access)In this paper, we reflect on the role of concepts in spatial planning as reproductive devices of discursive configurations. In contrast to instrumentalist interpretations of spatial concepts, we start from the idea that spatial planning concepts are inherently political. Building on post-structuralist strands of thought, we discuss the theoretical concepts of “empty signifier” and “master signifier” and instead, after analysis, put forward “open concepts”, in order to grasp the richness of meanings and functions of seemingly vague concepts. This manoeuvre allows us to analyse the trajectory and performance of the spatial concept of the “innovation campus” in the Netherlands. This, in turn, opens the door to an analysis of planning concepts as crystallization points and enablers of discursive configurations. The Dutch innovation campus is shown to be a result of a confluence of various national and international discourses, an open concept, flexible enough to enable the continuation of the planning game within the familiar set of coordinates. Because of the particular set of expectations associated with the innovation campus, promising structural change, it is bound to produce disappointment.17 oktober 201217 p