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

Thermodynamic Details of Pinholin S2168 Activation Revealed Using Alchemical Free Energy Simulations

Pinholin S2168 is a viral integral membrane protein whose function is to form nanoscopic "pinholes" in bacterial cell membranes to induce cell lysis as part of the viral replication cycle. Pinholin can transition from an inactive to an active conformation by exposing a transmembrane domain (TMD1) to the extracellular fluid. Upon activation, several copies of the protein assemble via interactions among a second transmembrane domain (TMD2) to form a single pore, thus hastening cell lysis and viral escape. The following experiments provide conformational descriptors of pinholin in active and inactive states and elucidate the molecular driving forces that control pinholin activity. In the present study, molecular dynamics (MD) simulations have been used to refine experimentally derived conformational descriptors into an atomistically detailed model of irsS2168, an antiholin mutant. To provide additional details about the thermodynamics of pinholin activation and to overcome large intrinsic kinetic barriers to activation, alchemical free energy simulations have been conducted. Alchemical mutations reveal the change in folding free energy upon mutation. The results suggest that alchemical mutations are an effective tool to rationalize experimental observations and predict the effects of site mutations on conformational states for proteins integrated into lipid bilayers. S16F, A17Q, A17Q+G21Q, and A17Q+G21Q+G14Q mutants reveal how changes in hydrophilicity and disruption of the glycine zipper motif influence pinholin's thermodynamic equilibrium, favoring the active conformation. These findings align with experimental observations from DEER spectroscopy, demonstrating that mutations increasing the hydrophilicity of TMD1 promote activation by making TMD1 more likely to exit the membrane and enter the extracellular fluid.

Adaptive accelerated reactive molecular dynamics driven by parallel collective variables overcoming dimensionality explosion

ReaxFF reactive molecular dynamics has significantly advanced the exploration of chemical reaction mechanisms in complex systems. However, it faces several challenges: (1) the prevalent use of excessively high temperatures (>2000 K), (2) a time scale considerably shorter than the experimental timeframes (nanoseconds vs seconds), and (3) the constraining impact of dimensionality growth due to collective variables on the expansiveness of research systems. To overcome these issues, we introduced Parallel Collective Variable-Driven Adaptive Accelerated Reaction Molecular Dynamics (PCVR), which integrates metadynamics with ReaxFF. This method incorporates bond distortion based on each bond type for customized Collective Variable (CV) parameterization, facilitating independent parallel acceleration. Simultaneously, the sampling was confined to fixed cutoff ranges for distinct bond distortions, effectively overcoming the challenge of the CV dimensionality explosion. This extension enhances the applicability of ReaxFF to non-strongly coupled systems with numerous reaction energy barriers and mitigates the system size limitations. Using accelerated reactive molecular dynamics, the oxidation of ester-based oil was simulated with 31 808 atoms at 500 K for 64 s. This achieved 61% efficiency compared to the original ReaxFF and was ∼37 times faster than previous methods. Unlike ReaxFF's high-temperature constraints, PCVR accurately reveals the pivotal role of oxygen in ester oxidation at industrial temperatures, producing polymers consistent with the sludge formation observed in ester degradation experiments. This method promises to advance reactive molecular dynamics by enabling simulations at lower temperatures, extending to second-level timescales, and accommodating systems with millions of atoms.

Curing and Cross-Linking Processes in the Poly(3,3-bis-azidomethyl oxetane)-tetrahydrofuran/Toluene Diisocyanate/Trimethylolpropane System: A Density Functional Theory and Accelerated ReaxFF Molecular Dynamics Investigation

The physical and chemical properties of solid propellant are influenced by the composition and structure of the binder, with its network structure being formed through curing and cross-linking reactions. Therefore, understanding the mechanisms of these reactions is crucial. In this study, we investigated the curing and cross-linking mechanisms of poly(3,3-bis-azidomethyl oxetane)-tetrahydrofuran (PBT), toluene diisocyanate (TDI), and trimethylolpropane (TMP) using a combination of density functional theory (DFT) calculations and accelerated ReaxFF molecular dynamics (MD) simulations. DFT calculations revealed that the steric effect of the -CH3 group in TDI exerts a significant influence on the curing reaction between TDI and PBT. Additionally, in the cross-linking process, the energy barrier for TDI reacting with TMP was found to be much lower than that for TDI reacting with the PBT-TDI intermediate. Subsequently, we conducted competing reaction processes of TMP/TDI-PBT-TDI cross-linking and TDI-PBT-TDI self-cross-linking using accelerated MD simulations within the fitted ReaxFF framework. The results showed that the successful frequency of TMP/TDI-PBT-TDI cross-linking was substantially higher than that of TDI-PBT-TDI self-cross-linking, consistent with the energy barrier results from DFT calculations. These findings deepen our understanding of the curing and cross-linking mechanisms of the PBT system, providing valuable insights for the optimization and design of solid propellants.

Predicting long-term trends in physical properties from short-term molecular dynamics simulations using long short-term memory

This study proposes a novel long short-term memory (LSTM)-based model for predicting future physical properties based on partial data of molecular dynamics (MD) simulation. It extracts latent vectors from atomic coordinates of MD simulations using graph convolutional network, utilizes LSTM to learn temporal trends in latent vectors and make one-step-ahead predictions of physical properties through fully connected layers. Validating with MD simulations of Ni solid-liquid systems, the model achieved accurate one-step-ahead prediction for time variation of the potential energy during solidification and melting processes using residual connections. Recursive use of predicted values enabled long-term prediction from just the first 20 snapshots of the MD simulation. The prediction has captured the feature of potential energy bending at low temperatures, which represents completion of solidification, despite that the MD data in short time do not have such a bending characteristic. Remarkably, for long-time prediction over 900 ps, the computation time was reduced to 1/700th of a full MD simulation of the same duration. This approach has shown the potential to significantly reduce computational cost for prediction of physical properties by efficiently utilizing the data of MD simulation.

Thermal degradation of greenhouse gas SF6 at realistic temperatures: Insights from atomic-scale CVHD simulations

Sulfur hexafluoride (SF6), recognized as a potent greenhouse gas with significant contributions to climate change, presents challenges in understanding its degradation processes. Molecular dynamics simulations are valuable tools for understanding modes of decomposition while the traditional approaches face limitations in time scale and require unrealistically high temperatures. The collective variable-driven hyperdynamics (CVHD) approach has been introduced to directly depict the pyrolysis process for SF6 gas at practical application temperatures, as low as 1600 K for the first time. Achieving an unprecedented acceleration factor of up to 107, the method extends the simulation time scale to milliseconds and beyond while maintaining consistency with experimental and theoretical models. The differences in the reaction process between simulations conducted at actual and elevated temperatures have been noted, providing insights into SF6 degradation pathways. The work provides a basis for the further studies on the thermal degradation of pollutants.

Roles of Accelerated Molecular Dynamics Simulations in Predictions of Binding Kinetic Parameters

Rational predictions on binding kinetics parameters of drugs to targets play significant roles in future drug designs. Full conformational samplings of targets are requisite for accurate predictions of binding kinetic parameters. In this review, we mainly focus on the applications of enhanced sampling technologies in calculations of binding kinetics parameters and residence time of drugs. The methods involved in molecular dynamics simulations are applied to not only probe conformational changes of targets but also reveal calculations of residence time that is significant for drug efficiency. For this review, special attention are paid to accelerated molecular dynamics (aMD) and Gaussian aMD (GaMD) simulations that have been adopted to predict the association or disassociation rate constant. We also expect that this review can provide useful information for future drug design.

Automatic Potential Energy Surface Exploration by Accelerated Reactive Molecular Dynamics Simulations: From Pyrolysis to Oxidation Chemistry

Automatic potential energy surface (PES) exploration is important to a better understanding of reaction mechanisms. Existing automatic PES mapping tools usually rely on predefined knowledge or computationally expensive on-the-fly quantum-chemical calculations. In this work, we have developed the PESmapping algorithm for discovering novel reaction pathways and automatically mapping out the PES using merely one starting species is present. The algorithm explores the unknown PES by iteratively spawning new reactive molecular dynamics (RMD) simulations for species that it has detected within previous RMD simulations. We have therefore extended the RMD simulation tool ChemTraYzer2.1 (Chemical Trajectory Analyzer, CTY) for this PESmapping algorithm. It can generate new seed species, automatically start replica simulations for new pathways, and stop the simulation when a reaction is found, reducing the computational cost of the algorithm. To explore PESs with low-temperature reactions, we applied the acceleration method collective variable (CV)-driven hyperdynamics. This involved the development of tailored CV templates, which are discussed in this study. We validate our approach for known pathways in various pyrolysis and oxidation systems: hydrocarbon isomerization and dissociation (C4H7 and C8H7 PES), mostly dominant at high temperatures and low-temperature oxidation of n-butane (C4H9O2 PES) and cyclohexane (C6H11O2 PES). As a result, in addition to new pathways showing up in the simulations, common isomerization and dissociation pathways were found very fast: for example, 44 reactions of butenyl radicals including major isomerizations and decompositions within about 30 min wall time and low-temperature chemistry such as the internal H-shift of RO2 → QO2H within 1 day wall time. Last, we applied PESmapping to the oxidation of the recently proposed biohybrid fuel 1,3-dioxane and validated that the tool could be used to discover new reaction pathways of larger molecules that are of practical use.

NWChem: Recent and Ongoing Developments

This paper summarizes developments in the NWChem computational chemistry suite since the last major release (NWChem 7.0.0). Specifically, we focus on functionality, along with input blocks, that is accessible in the current stable release (NWChem 7.2.0) and in the "master" development branch, interfaces to quantum computing simulators, interfaces to external libraries, the NWChem github repository, and containerization of NWChem executable images. Some ongoing developments that will be available in the near future are also discussed.

Kinetics from Metadynamics: Principles, Applications, and Outlook

Metadynamics is a popular enhanced sampling algorithm for computing the free energy landscape of rare events by using molecular dynamics simulation. Ten years ago, Tiwary and Parrinello introduced the infrequent metadynamics approach for calculating the kinetics of transitions across free energy barriers. Since then, metadynamics-based methods for obtaining rate constants have attracted significant attention in computational molecular science. Such methods have been applied to study a wide range of problems, including protein-ligand binding, protein folding, conformational transitions, chemical reactions, catalysis, and nucleation. Here, we review the principles of elucidating kinetics from metadynamics-like approaches, subsequent methodological developments in this area, and successful applications on chemical, biological, and material systems. We also highlight the challenges of reconstructing accurate kinetics from enhanced sampling simulations and the scope of future developments.

Improved reweighting protocols for variationally enhanced sampling simulations with multiple walkers

In molecular dynamics simulations utilizing enhanced-sampling techniques, reweighting is a central component for recovering the targeted ensemble averages of the "unbiased" system by calculating and applying a bias-correction function c(t). We present enhanced reweighting protocols for variationally enhanced sampling (VES) simulations by exploiting a recent reweighting method, originally introduced in the metadynamics framework [Giberti et al. J. Chem. Theory Comput., 2020, 16, 100-107], which was modified and extended to multiple-walker simulations: these may be implemented either as "independent" walkers (associated with one unique correction function per walker) or "cooperative" ones that all share one correction function, which is the hitherto only explored option. When each case is combined with the two possibilities of determining c(t) by time integration up to either t or over the entire simulation period , altogether four reweighting options result. Their relative merits were assessed by well-tempered VES simulations of two model problems: locating the free-energy difference between two metastable molecular conformations of the N-acetyl-L-alanine methylamide dipeptide, and the recovery of an a priori known distribution when one water molecule in the liquid phase is perturbed by a periodic free-energy function. The most rapid convergence occurred for large cooperative walkers, regardless of the upper integration limit, but integrating up to t proved advantageous for small walker ensembles. That novel reweighting method compared favorably to the standard VES reweighting, as well as to current state-of-the-art reweighting options introduced for metadynamics simulations that estimate c(t) by integration over the collective variables. For further gains in computational speed and accuracy, we also introduce analytical solutions for c(t), as well as offering further insight into its features by approximative analytical expressions in the "high-temperature" regime.

Investigation of Polymer Aging Mechanisms Using Molecular Simulations: A Review

Aging has a serious impact on the properties of functional polymers. Therefore, it is necessary to study the aging mechanism to prolong the service and storage life of polymer-based devices and materials. Due to the limitations of traditional experimental methods, more and more studies have adopted molecular simulations to analyze the intrinsic mechanisms of aging. In this paper, recent advances in molecular simulations of the aging of polymers and their composites are reviewed. The characteristics and applications of commonly used simulation methods in the study of the aging mechanisms (traditional molecular dynamics simulation, quantum mechanics, and reactive molecular dynamics simulation) are outlined. The current simulation research progress of physical aging, aging under mechanical stress, thermal aging, hydrothermal aging, thermo-oxidative aging, electric aging, aging under high-energy particle impact, and radiation aging is introduced in detail. Finally, the current research status of the aging simulations of polymers and their composites is summarized, and the future development trend has been prospected.

A Reactive Molecular Dynamics Study of Chlorinated Organic Compounds. Part I: Force Field Development

This work presents a novel parametrization for the ReaxFF formalism as a means to investigate reaction processes of chlorinated organic compounds. Force field parameters cover the chemical elements C, H, O, Cl and were obtained using a novel optimization approach involving relaxed potential energy surface scans as training targets. The resulting ReaxFF parametrization shows good transferability, as demonstrated on two independent ab initio validation sets. While this first part of our two-paper series focuses on force field parametrization, we apply our parameters to the simulation of chlorinated dibenzofuran formation and decomposition processes in Part II.

Inhomogeneity Effects on Reactions in Supercritical Fluids: A Computational Study on the Pyrolysis of n-Decane

Supercritical fluids exhibit peculiar inhomogeneity, which strongly affects reaction behaviors in them. However, explanations for inhomogeneity and its effect on reactions are both ambiguous so far. Here, we provide an atomic-level understanding of inhomogeneity effects on reactions via the computational method, with the example of n-decane pyrolysis under supercritical conditions. We describe the characteristic pyrolysis behaviors through collective variable-driven hyperdynamics (CVHD) simulations and explain the inhomogeneity of supercritical n-decane as the coexistence of gas-like and liquid-like atoms by a trained machine learning classifier. Due to their specific local environment, the appearance of liquid-like atoms under supercritical conditions significantly increases the type and frequency of bimolecular reactions and eventually causes changes in product distributions. Future research with this method is expected to extend the effect of inhomogeneity on other reactions under supercritical conditions or other condensed phases.

Predicting Solute Diffusivity in Polymers Using Time-Temperature Superposition

We demonstrate a novel application of the time-temperature superposition (TTS) principle to predict solute diffusivity D in glassy polymers using atomistic molecular dynamics simulations. Our TTS approach incorporates the Debye-Waller factor ⟨u²⟩, a measure of solute caging, along with concepts from thermodynamic scaling methods, allowing us to balance contributions to the dynamics from temperature and ⟨u²⟩ using adjustable parameters. Our approach rescales the solute mean-squared displacement curves at several temperatures into a master curve that approximates the diffusive dynamics at a reference temperature, effectively extending the simulation time scale from nanoseconds to seconds and beyond. With a set of "universal" parameters, this TTS approach predicts D with reasonable accuracy in a broad range of polymer/solute systems. Using TTS greatly reduces the computational cost compared to standard MD simulations. Thus, our method offers a means to rapidly and routinely provide order-of-magnitude estimates of D using simulations.

Efficient Reaction Space Exploration with ChemTraYzer-TAD

The development of a reaction model is often a time-consuming process, especially if unknown reactions have to be found and quantified. To alleviate the reaction modeling process, automated procedures for reaction space exploration are highly desired. We present ChemTraYzer-TAD, a new reactive molecular dynamics acceleration technique aimed at efficient reaction space exploration. The new method is based on the basin confinement strategy known from the temperature-accelerated dynamics (TAD) acceleration method. Our method features integrated ChemTraYzer bond-order processing steps for the automatic and on-the-fly determination of the positions of virtual walls in configuration space that confine the system in a potential energy basin. We use the example of 1,3-dioxolane-4-hydroperoxide-2-yl radical oxidation to show that ChemTraYzer-TAD finds more than 100 different parallel reactions for the given set of reactants in less than 2 ns of simulation time. Among the many observed reactions, ChemTraYzer-TAD finds the expected typical low-temperature reactions despite the use of extremely high simulation temperatures up to 5000 K. Our method also finds a new concerted β-scission plus O₂ addition with a lower reaction barrier than the literature-known and so-far dominant β-scission.

Decomposition Mechanism of Isoprenoid Hydrocarbon p-Menthane in the Presence of Pt@FGS Nanoparticles: A ReaxFF-MD Study

Pt@FGS nanoparticles have shown effective enhancement in the decomposition of hydrocarbon fuels. To further explore the potential enhancing mechanisms of Pt@FGS nanoparticles, the catalytic decomposition of p-menthane, a bioderived isoprenoid "drop-in" fuel with great promise, is investigated here using the reactive force-field molecular dynamics (ReaxFF-MD) simulations. The results show that the Pt@FGS nanoparticles exhibit good catalytic reactivity with a reduction of the activation energy by nearly 62%. Possible initial reactions of enhanced p-menthane (PMT) decomposition are discussed, which suggests that the supported Pt-cluster plays a key role in the dehydrogenation of PMT, as does the oxygen-containing functional group of the functionalized graphene sheets (FGS). It is also interesting to note that the presence of Pt@FGS causes the initial reactions, which are dominated by H-abstraction, favorable in both kinetics and thermodynamics.

Hyperdynamics simulations with ab initio forces

By applying the locally optimal rotation method to deal with the lowest eigenvalue of a Hessian matrix, we have efficiently incorporated the hyperdynamics method into the ab initio scheme. In the present method, we only need to calculate the first derivative of the potential and several more force calls in each molecular dynamics (MD) step, which makes hyperdynamics simulation applicable in ab initio MD simulations. With this implementation, we are able to simulate defect diffusion in silicon with boost factors up to 10⁵. We utilized both direct MD and the hyperdynamics method to investigate diffusion of lithium atoms and silicon vacancies in silicon. We identified the complex diffusion process. The obtained diffusion coefficients of Li atoms and Si vacancies are in good agreement with the direct MD results.

Free energy barriers from biased molecular dynamics simulations

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.

Molecular Hyperdynamics Coupled with the Nonorthogonal Tight-Binding Approach: Implementation and Validation

We present the molecular hyperdynamics algorithm and its implementation to the nonorthogonal tight-binding model NTBM and the corresponding software. Due to its multiscale structure, the proposed approach provides the long time scale simulations (more than 1 s), unavailable for conventional molecular dynamics. No preliminary information about the system's potential landscape is needed for the use of this technique. The optimal interatomic potential modification is automatically derived from the previous simulation steps. The average time between adjusted potential energy fluctuations provides an accurate evaluation of physical time during the hyperdynamics simulation. The main application of the presented hyperdynamics method is the study of thermal-induced defects arising in the middle-sized or relatively large atomic systems at low temperatures. To validate the presented method, we apply it to the C₆₀ cage and its derivative C₆₀NH₂. Hyperdynamics leads to the same results as a conventional molecular dynamics, but the former possesses much higher performance and accuracy due to the wider temperature region. The coefficient of acceleration achieves 10⁷ and more.

Insights Into the Resistance Mechanisms of Inhibitors to FLT3 F691L Mutation via an Integrated Computational Approach

Research has shown that FMS-like tyrosine kinase 3 (FLT3) may be a vital drug target for acute myeloid leukemia (AML). However, even though the clinically relevant F691L gatekeeper mutation conferred resistance to current FLT3 drug quizartinib, PLX3397 remained unaffected. In this study, the protein–ligand interactions between FLT3 kinase domain (wild-type or F691L) and quizartinib or PLX3397 were compared via an integrated computational approach. The classical molecular dynamics (MD) simulations in conjunction with dynamic cross-correlation (DCC) analysis, solvent-accessible surface area (SASA), and free energy calculations indicated that the resistant mutation may induce the conformational change of αC-helix and A-loop of the FLT3 protein. The major variations were controlled by the electrostatic interaction and SASA, which were allosterically regulated by residues Glu-661 and Asp-829. When FLT3-F691L was bound to quizartinib, a large conformational change was observed via combination of accelerated MD simulations (aMDs), principal component analysis (PCA), and free energy landscape (FEL) calculations. The umbrella sampling (US) simulations were applied to investigate the dissociation processes of the quizartinib or PLX3397 from FLT3-WT and FLT3-F691L. The calculated results suggested that PLX3397 had similar dissociation processes from both FLT3-WT and FLT3-F691L, but quizartinib dissociated more easily from FLT3-F691L than from FLT3-WT. Thus, reduced residence time was responsible for the FLT3-F691L resistance to inhibitors. These findings indicated that both the conformational changes of αC-helix and A-loop and the drug residence time should be considered in the design of drugs so that rational decisions can be made to overcome resistance to FLT3-F691L.

Towards operando computational modeling in heterogeneous catalysis

An increased synergy between experimental and theoretical investigations in heterogeneous catalysis has become apparent during the last decade. Experimental work has extended from ultra-high vacuum and low temperature towards operando conditions. These developments have motivated the computational community to move from standard descriptive computational models, based on inspection of the potential energy surface at 0 K and low reactant concentrations (0 K/UHV model), to more realistic conditions. The transition from 0 K/UHV to operando models has been backed by significant developments in computer hardware and software over the past few decades. New methodological developments, designed to overcome part of the gap between 0 K/UHV and operando conditions, include (i) global optimization techniques, (ii) ab initio constrained thermodynamics, (iii) biased molecular dynamics, (iv) microkinetic models of reaction networks and (v) machine learning approaches. The importance of the transition is highlighted by discussing how the molecular level picture of catalytic sites and the associated reaction mechanisms changes when the chemical environment, pressure and temperature effects are correctly accounted for in molecular simulations. It is the purpose of this review to discuss each method on an equal footing, and to draw connections between methods, particularly where they may be applied in combination.

Initial stage of atomic layer deposition of 2D-MoS2 on a SiO2 surface: a DFT study

In this study, we investigate the reactions involving Atomic Layer Deposition (ALD) of 2D-MoS2 from the heteroleptic precursor Mo(NMe2)2(NtBu)2 and H2S as the co-reagent on a SiO2(0001) surface by means of density functional theory (DFT). All dominant reaction pathways from the early stage of adsorption of each ALD reagent to the formation of bulk-like Mo and S at the surface are identified. In the metal pulse, proton transfer from terminal OH groups on the SiO2 to the physisorbed metal precursor increases the Lewis acidity of Mo and Lewis basicity of O, which gives rise to the chemical adsorption of the metal precursor. Proton transfer from the surface to the dimethylamido ligands leads to the formation and desorption of dimethylamine. In contrast, the formation and desorption of tert-butylamine is not energetically favorable. The tert-butylimido ligand can only be partially protonated in the metal pulse. In the sulphur pulse, co-adsorption and dissociation of H2S molecules give rise to the formation and desorption of tert-butylamine. Through the calculated activation energies, the cooperation between H2S molecules ('cooperative' mechanism) is shown to have a profound influence on the formation and desorption of tert-butylamine, which are crucial steps in the initial ALD deposition of 2D-MoS2 on SiO2. The cyclic ALD reactions give rise to the formation of a buffer layer which might have important consequences for the electrical and optical properties on the 2D layer formed in the subsequent homodeposition.

Atomistic simulations of graphite etching at realistic time scales

Hydrogen-graphite interactions are relevant to a wide variety of applications, ranging from astrophysics to fusion devices and nano-electronics. In order to shed light on these interactions, atomistic simulation using Molecular Dynamics (MD) has been shown to be an invaluable tool. It suffers, however, from severe time-scale limitations. In this work we apply the recently developed Collective Variable-Driven Hyperdynamics (CVHD) method to hydrogen etching of graphite for varying inter-impact times up to a realistic value of 1 ms, which corresponds to a flux of ∼1020 m-2 s-1. The results show that the erosion yield, hydrogen surface coverage and species distribution are significantly affected by the time between impacts. This can be explained by the higher probability of C-C bond breaking due to the prolonged exposure to thermal stress and the subsequent transition from ion- to thermal-induced etching. This latter regime of thermal-induced etching - chemical erosion - is here accessed for the first time using atomistic simulations. In conclusion, this study demonstrates that accounting for long time-scales significantly affects ion bombardment simulations and should not be neglected in a wide range of conditions, in contrast to what is typically assumed.

Direct evidence of atomic-scale structural fluctuations in catalyst nanoparticles

Rational catalyst design requires an atomic scale mechanistic understanding of the chemical pathways involved in the catalytic process. A heterogeneous catalyst typically works by adsorbing reactants onto its surface, where the energies for specific bonds to dissociate and/or combine with other species (to form desired intermediate or final products) are lower. Here, using the catalytic growth of single-walled carbon nanotubes (SWCNTs) as a prototype reaction, we show that the chemical pathway may in-fact involve the entire catalyst particle, and can proceed via the fluctuations in the formation and decomposition of metastable phases in the particle interior. We record in situ and at atomic resolution, the dynamic phase transformations occurring in a Cobalt catalyst nanoparticle during SWCNT growth, using a state-of-the-art environmental transmission electron microscope (ETEM). The fluctuations in catalyst carbon content are quantified by the automated, atomic-scale structural analysis of the time-resolved ETEM images and correlated with the SWCNT growth rate. We find the fluctuations in the carbon concentration in the catalyst nanoparticle and the fluctuations in nanotube growth rates to be of complementary character. These findings are successfully explained by reactive molecular dynamics (RMD) simulations that track the spatial and temporal evolution of the distribution of carbon atoms within and on the surface of the catalyst particle. We anticipate that our approach combining real-time, atomic-resolution image analysis and molecular dynamics simulations will facilitate catalyst design, improving reaction efficiencies and selectivity towards the growth of desired structure.

Assessing Generic Collective Variables for Determining Reaction Rates in Metadynamics Simulations

A persistent challenge in using the metadynamics method is deciding which degrees of freedom, or collective variables, should be biased because these selections are not obvious and require intuition about the system being studied. There are, however, collective variables, which can be constructed with only basic knowledge about the system studied, that provide an opportunity to alleviate this issue. We simulated two different reacting systems where two types of such collective variables (SPRINT coordinates and the collective variable-driven hyperdynamics method) were biased following the infrequent metadynamics method in order to recover the rates of reactions. We demonstrate that both generic collective variables are capable of reproducing the reaction rates of both systems and can enhance the efficiency of the simulation when compared to typical collective variables.

A DFT study of H-dissolution into the bulk of a crystalline Ni(111) surface: a chemical identifier for the reaction kinetics

In this study, we investigated the diffusion of H-atoms to the subsurface and their further diffusion into the bulk of a Ni(111) crystal by means of density functional theory calculations in the context of thermal and plasma-assisted catalysis.

Direct observation of realistic-temperature fuel combustion mechanisms in atomistic simulations

Advanced accelerated molecular dynamics simulations provide a detailed atomic-level picture of combustion at realistic temperatures and pressures.

Atomistic simulations can in principle provide an unbiased description of all mechanisms, intermediates, and products of complex chemical processes. However, due to the severe time scale limitation of conventional simulation techniques, unrealistically high simulation temperatures are usually applied, which are a poor approximation of most practically relevant low-temperature applications. In this work, we demonstrate the direct observation at the atomic scale of the pyrolysis and oxidation of n-dodecane at temperatures as low as 700 K through the use of a novel simulation technique, collective variable-driven hyperdynamics (CVHD). A simulated timescale of up to 39 seconds is reached. Product compositions and dominant mechanisms are found to be strongly temperature-dependent, and are consistent with experiments and kinetic models. These simulations provide a first atomic-level look at the full dynamics of the complicated fuel combustion process at industrially relevant temperatures and time scales, unattainable by conventional molecular dynamics simulations.

Atomic scale simulation of carbon nanotube nucleation from hydrocarbon precursors

Atomic scale simulations of the nucleation and growth of carbon nanotubes is essential for understanding their growth mechanism. In spite of over twenty years of simulation efforts in this area, limited progress has so far been made on addressing the role of the hydrocarbon growth precursor. Here we report on atomic scale simulations of cap nucleation of single-walled carbon nanotubes from hydrocarbon precursors. The presented mechanism emphasizes the important role of hydrogen in the nucleation process, and is discussed in relation to previously presented mechanisms. In particular, the role of hydrogen in the appearance of unstable carbon structures during in situ experimental observations as well as the initial stage of multi-walled carbon nanotube growth is discussed. The results are in good agreement with available experimental and quantum-mechanical results, and provide a basic understanding of the incubation and nucleation stages of hydrocarbon-based CNT growth at the atomic level.

Atomic scale simulation of the nucleation and growth of carbon nanotubes is essential for understanding their growth mechanism. Here, the authors look at cap nucleation of nanotubes from hydrocarbon precursors, specifically probing the role of hydrogen in the early stages of growth.