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Results for “Kinetic Monte Carlo”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Consequences of adsorbate-adsorbate interactions for apparent kinetics of surface catalytic reactions

Lateral adsorbate interactions at catalyst surfaces are known to influence adsorption energies and reaction rates. Lattice-based kinetic Monte Carlo (kMC) simulations are able to capture these influences, but such models are typically parameterized for a specific reaction network and catalyst surface. Here we report kMC simulations to probe the influence of lateral interactions on simulated rates, rate orders, apparent activation energies, and Sabatier plots. We construct a simple, two-step reaction network involving a single adsorbate and rate- limiting diatomic dissociation, employ a generic repulsive lateral interaction model consistent with known adsorbate-adsorbate interactions on metal surfaces, and a rate model consistent with known Brønsted-Evans-Polyani relationships for diatomic dissocations. Furthermore, we juxtapose reaction kinetics over a wide range of reaction conditions and catalyst binding energies, as a function of interaction strength. We find that at a given zero-coverage binding energy and external conditions, adsorbate coverage decreases monotonically with increasing interaction strength, but absolute rates can vary linearly or nonlinearly. Interactions flatten the Sabatier volcano and shift the maximum towards stronger binding. Influences on apparent rate orders and activation energies are modest and are sensitive to interaction-induced adsorbate ordering. Model predictions are sensitive to lattice size effects at high coverages. The results, modelled for a simple reaction system, highlight the generic consequences of lateral interactions and guidance for identifying their signatures in observed kinetics.

kinetic Monte Carlo↗
DOE OSTI2020

An active learning high-throughput microstructure calibration framework for solving inverse structure–process problems in materials informatics

Determining a process–structure–property relationship is the holy grail of materials science, where both computational prediction in the forward direction and materials design in the inverse direction are essential. Problems in materials design are often considered in the context of process–property linkage by bypassing the materials structure, or in the context of structure–property linkage as in microstructure-sensitive design problems. However, there is a lack of research effort in studying materials design problems in the context of process–structure linkage, which has a great implication in reverse engineering. In this paper, given a target microstructure, we propose an active learning high-throughput microstructure calibration framework to derive a set of processing parameters, which can produce an optimal microstructure that is statistically equivalent to the target microstructure. The proposed framework is formulated as a noisy multi-objective optimization problem, where each objective function measures a deterministic or statistical difference of the same microstructure descriptor between a candidate microstructure and a target microstructure. Furthermore, to significantly reduce the physical waiting wall-time, we enable the high-throughput feature of the microstructure calibration framework by adopting an asynchronously parallel Bayesian optimization to exploit high-performance computing resources. Case studies in additive manufacturing and grain growth are used to demonstrate the applicability of the proposed framework, where kinetic Monte Carlo (kMC) simulation is used as a forward predictive model, such that for a given target microstructure, the target processing parameters that produced this microstructure are successfully recovered.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Anomaly Detection in Materials Digital Twins with Multiscale ICME for Additive Manufacturing

Detecting anomaly in fatigue and fracture experimental materials science is an interesting yet challenging topic. The reasons are threefold. First, the anomalous microstructure feature that gives rise to structural failure is small, sometimes in the order of 10 -7 of the interrogated volume. This, in turn, results in a highly imbalanced classification problem in machine learning (ML). Second, the consequence is high, in the sense that the test specimen is destructed in such case. Third, the convolution between microstructure stochasticity and the small probability of void nucleation, growth, and coalescence makes failure and fracture a hard-to-predict and challenging problem in materials science due to its irreproducibility, even experimentally. In this paper, we developed a materials digital twin and applied anomaly detection methods to detect voids and anomaly in additive manufacturing (AM). The materials digital twin is driven by two integrated computational materials engineering (ICME) models, which are kinetic Monte Carlo (kMC) and crystal plasticity finite element method (CPFEM). In conclusion, we demonstrated that by using anomaly detection, it is possible to detect voids and other defects in materials digital twin, which paves way for future research in integrating materials digital twin with its physical counterpart.

ICME↗
DOE OSTI2024

Phase transformation mechanism in irradiation-induced superlattice formation

Atomic kinetic Monte Carlo simulations were used to model void superlattice formation under irradiation in molybdenum, driven by anisotropic diffusion of self-interstitial atoms. A change in the phase transformation mechanism from nucleation and growth to spinodal decomposition occurred with increasing dose rate, with both mechanisms leading to superlattice formation. Analysis of a rate-theory based analytical model showed that an observed change in the kinetics of vacancy accumulation, the appearance of a region of positive second derivative in the plot of average vacancy concentration versus time, was caused by the onset of spinodal instability. Further, the analytical model showed that for molybdenum and several other metals where void superlattice formation is commonly observed, the phase transformation likely occurs by nucleation and growth. However, nickel may offer the possibility of experimental observation of the transition between phase transformation mechanisms.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Ethanol Conversion over La 0.7 Sr 0.3 MnO 3–x (100): Autocatalysis, Adjacent O-Vacancies, Disproportionation, and Dehydrogenation

The mechanism for catalytic conversion of ethanol over La 0.7 Sr 0.3 MnO 3– x (100) surface to acetaldehyde and ethene was investigated. Pre-exposure temperature-programmed reaction (PE-TPR) experiments were performed in which ethanol was introduced to oxidized or reduced surfaces followed by heating. In particular, sequential PE-TPR experiments were conducted to incrementally and gradually reduce the surface. The products and their ratios were investigated as a function of surface reduction. The data show that acetaldehyde and ethene production is catalyzed with hydrogen abstraction and oxygen abstraction reactions occurring by intermediates in vacancies at various temperatures >400 K. Adsorption of acetaldehyde followed by a temperature-programmed reaction does not produce ethene, indicating that acetaldehyde is not an intermediate to ethene and that the hydrogen and oxygen abstraction from ethanol to ethene are decoupled steps. Further evidence for this mechanistic nuance was obtained using isotopically labeled ethanol (CD 3 CH 2 OH), which produces CD 3 CHO and CD 2 CH 2 . Additionally, the ratio of aldehyde production to alkene production increases with reduction, suggesting that aldehyde is produced from a disproportionation reaction between ethoxy species in adjacent O-vacancies, while ethene is produced from a dehydrogenation reaction with ethoxy species in vacancies without requiring adjacent O-vacancies. Counterintuitively, this finding indicates that the more oxygenated product (aldehyde vs ethene) is favored with more vacancies and that the net alcohol conversion is autocatalytic. Density functional theory calculations were able to find the previously unknown disproportionation pathway between ethoxies in adjacent O-vacancies, and kinetic Monte Carlo simulations support this interpretation by reproducing experimental selectivities. The activation energies for these pathways are estimated as 132 ± 10 kJ/mol for the disproportionation reaction (when occurring between ethoxies in adjacent vacancies) and as 148 ± 11 kJ/mol for the direct dehydrogenation reaction of an ethoxy in a vacancy. Based on these results, a mechanism with operative pathways based on elementary steps in O-vacancies is reported.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

GrainPaint: A multi-scale diffusion-based generative model for microstructure reconstruction of large-scale objects

Simulation-based approaches to microstructure generation can suffer from a variety of limitations, such as high memory usage, long computational times, and difficulties in generating complex geometries. Generative machine learning models present a way around these issues, but they have previously been limited by the fixed size of their generation area. Here, we present a new microstructure generation methodology leveraging advances in inpainting using denoising diffusion models to overcome this generation area limitation. We show that microstructures generated with the presented methodology are statistically similar to grain structures generated with a kinetic Monte Carlo simulator, SPPARKS.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Pilgrim : A thermal rate constant calculator and a chemical kinetics simulator

Pilgrim is a program written in Python and designed to use direct dynamics in the calculation of thermal rate constants of chemical reactions by the variational transition state theory (VTST), based on electronic structure calculations for the potential energy surface. Pilgrim can also simulate reaction mechanisms using kinetic Monte Carlo (KMC). For reaction processes with many elementary steps, the rate constant of each of these steps can be calculated by means of conventional transition state theory (TST) or by using VTST. In the current version, Pilgrim can evaluate thermal rates using the canonical version of reaction-path VTST, which requires the calculation of the minimum energy path (MEP) associated with each elementary step or transition structure. Multi-dimensional quantum effects can be incorporated through the small-curvature tunneling (SCT) approximation. These methodologies are available both for reactions involving a single structure of the reactants and the transition state and also for reactions involving flexible molecules with multiple conformations of the reactant and/or of the transition state. For systems with many conformers, the program can evaluate each of the elementary reaction rate constants by multipath canonical VTST or multi-structural VTST. Moreover, the reactant can be unimolecular or bimolecular. Torsional anharmonicity can be incorporated through either the MSTor or the Q2DTor programs. Dual-level calculations are also available in Pilgrim: automatic high-level single-point energies can be used to correct the energy of reactants, transition states, products, and MEP points using the interpolated single-point energies (ISPE) algorithm. When the rate constants of all the chemical processes of interest are known, by means of their calculation using Pilgrim or alternatively through analytical fits to the rate constants as functions of temperature, it is possible to simulate a multistep mechanism under specified laboratory conditions using KMC. Finally, this algorithm allows performing a kinetic simulation to monitor the evolution of each chemical species with time and obtain the product yields.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Effect of Helium Flux on Near-surface Helium Accumulation in Plasma-Exposed Tungsten

We report results of object kinetic Monte Carlo (OKMC) simulations aimed at understanding the effect of helium flux on the near-surface helium accumulation in plasma-facing tungsten, which is initially defect-free and has a W(100) surface orientation. These OKMC simulations are performed at 933 K for fluxes ranging from 10 22 to 4 × 10 25 He/m 2 s, with 100 eV helium atoms impinging on a W(100) surface up to a maximum fluence of 4×10 19 He/m 2 . In the near-surface region, helium clusters interact elastically with the free surface. The interaction is attractive and results in the drift of mobile helium clusters towards the surface as well as increased trap mutation rates. The associated kinetics and energetics of the above-mentioned processes obtained from molecular dynamics simulations are also considered. The OKMC simulations indicate that as the flux decreases, the retention of implanted helium decreases, and its depth distribution shifts to deeper below the surface in initially pristine tungsten. Furthermore, the fraction of retained helium diffusing into the bulk increases as well, so much so that for 10 22 He/m 2 s, almost all of the retained helium diffused into the bulk with minimal/negligible near-surface helium accumulation. At a given flux, with increasing fluence, the fraction of retained helium initially decreases and then starts to increase after reaching a minimum. The occurrence of the retention minimum shifts to higher fluences as the flux decreases. Although the near-surface helium accumulation spreads deeper into the material with decreasing flux and increasing fluence, the spread appears to saturate at depths between 80 and 100 nm. Finally, we present a detailed analysis of the influence of helium flux on the size and depth distribution of total helium and helium bubbles.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Exascale granular microstructure reconstruction in 3D volumes of arbitrary geometries with generative learning

Reconstructing 3D granular microstructures within volumes of arbitrary geometries from limited 2D image data is crucial for predicting the material properties, as well as performances of structural components accounting for material microstructural effects. We present a novel generative learning framework that enables exascale reconstruction of granular microstructures within complex 3D geometric volumes. Building upon existing transfer learning techniques using pre-trained convolutional neural networks (CNN), we introduce several key innovations to overcome the difficulties inherent in arbitrary geometries. Our framework incorporates periodic boundary conditions using circular padding techniques, ensuring continuity and representativeness of the reconstructed microstructures. We also introduce a novel seamless transition reconstruction (STR) method that creates statistically equivalent transition zones to integrate multiple pre-existing 3D microstructure volumes. Based on STR, we propose a cost-effective strategy for reconstructing microstructures within complex geometric volumes, minimizing computational waste. Validation through numerical experiments using kinetic Monte Carlo simulations demonstrates accurate reproduction of grain statistics, including grain size distributions and morphology. A case study involving the reconstruction of a 4-blade propeller microstructure illustrates the method’s capability to efficiently handle complex geometries. In conclusion, the proposed framework significantly reduces computational demands while maintaining high reconstruction quality, paving the way for scalable microstructure reconstruction in materials design and analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Radiation-reduced diffusion of Nd in bcc Fe

Here, this study combines ab initio calculations and kinetic Monte Carlo simulations to investigate the bulk diffusion of Nd, an exemplar lanthanide element, in bcc Fe. Contrary to the conventional belief that irradiation accelerates diffusion processes, our study reveals that irradiation can unexpectedly reduce the diffusion of Nd due to the strong binding between substitutional Nd atoms and vacancies in bcc Fe. Combined with the high concentration of nonequilibrium vacancies, this promotes the abundant formation of Nd+3Va defect complexes containing a single Nd substitutional atom and three vacancies, accompanied by the depletion of Nd-vacancy pairs that mediate the diffusion of Nd.

Density functional theory (DFT)↗
DOE OSTI2020

Effect of Confinement of SIA Cluster Diffusion by Impurities on Radiation Defect Accumulation due to 14MeV neutrons in Tungsten

Impurities acting as traps, reduce the self-interstitial atom (SIA) cluster mobility, thereby increasing recombination and in turn enhancing radiation-resistance of polycrystalline materials. However, at elevated temperatures, depending on SIA-trap (impurity) binding energy, SIA clusters may trap and detrap (detach) numerous times. In tungsten, SIA clusters of all sizes glide one-dimensionally (1D) along their Burgers vector direction. Hence, detrapped SIAs would retrace their path, confining their 1D-glide between traps. However, small SIA clusters can change the direction of their 1D-glide by overcoming a rotation barrier. In addition, impurities or solutes are known to reduce this barrier. A lower rotation barrier can result in detrapped SIA clusters that diffuse in a random 1D direction, effectively leading to 3D (net-3D) diffusion. Here we investigate the effect of two cases of SIA diffusion, namely confined-1D diffusion and net-3D diffusion, on the damage accumulation in polycrystalline tungsten under 14 MeV neutron irradiation using the object kinetic Monte Carlo method. Simulations were performed using cascade debris obtained from molecular dynamics simulations with primary knock-on atom (PKA) energies corresponding to 14 MeV neutrons. In the simulations with net-3D diffusion, SIA clusters of all sizes diffuse in a random 1D direction after the clusters detrap. While for the confined 1D diffusion case, SIA clusters larger than size 5 retain their original direction of 1D-glide. In both types of simulations, trapped vacancies are assumed to be permanently immobilized. Unexpectedly, the damage accumulation was lower with confined 1D diffusion than that with net-3D. We present a systematic comparison of the influence of the two cases of SIA diffusion on the radiation damage accumulation as a function of dose rate, detrapping barrier, and impurity concentration at 1025 K.

Accumulation and recovery of lattice damage, tungs↗
DOE OSTI2025

Rate Limiting Regimes in Photochemical H2 Generation by Complexes of Colloidal CdS Nanorods and Hydrogenase

Driving redox enzyme catalysis with photoexcited semiconductor nanocrystals is a compelling approach for chemical conversion. We examined how the interplay of the many chemical steps involved determines the rates of photochemical H2 production with complexes of colloidal CdS nanorods and an [FeFe]-hydrogenase. We elucidated the roles of three critical and previously elusive processes-scavenging of photoexcited holes from nanorods, back-electron transfer, and H2 oxidation. Kinetic Monte Carlo simulations and fitting to experimental data revealed that hole transfer becomes the rate-limiting step at high illumination intensities. Comparisons of simulations to experimental H2 production showed that both back-electron transfer and H2 oxidation play an efficiency-limiting role at high catalyst loadings. This work provides guiding principles for tuning experimental parameters to minimize energy-wasting pathways and optimize photochemical product formation. More broadly, we demonstrate how critical but elusive chemical steps in photochemical reactions can be probed with a combination of experiments and simulations.

08 HYDROGEN↗
DOE OSTI2024

CO–induced roughening of Cu(111): formation and detection of reactive nanoclusters on metal surfaces

The formation of nanoclusters on metal surfaces in the presence of reactive environments is a phenomenon with important implications for catalysis. These nanoclusters are composed of atoms ejected from undercoordinated sites such as step edges, and their presence alters the catalytic properties of solid materials. We perform density functional theory (DFT) and kinetic Monte Carlo (KMC) simulations to investigate the formation and reactivity of copper clusters on Cu(111). Our results indicate a considerably higher reactivity of small copper nanoclusters, with up to seven atoms in size on roughened copper surfaces than on pristine Cu(111) and Cu(211). Regarding the restructuring events that give rise to nanoclusters under CO atmospheres, we determine that the ejection of Cu atoms from step edges and their migration therefrom to adjacent Cu(111) terraces are, by and large, driven by CO coverage effects. By means of KMC simulations, which account for CO–CO lateral interactions and CO–induced surface restructuring, we show that temperature programmed desorption (TPD) holds promise for the detection of highly reactive nanoclusters. Furthermore, our approach showcases how surface restructuring and surface–adsorbate bond breaking can be combined when modeling surface reactions and contributes to the development of an advanced understanding of the nature of active site under reaction conditions.

catalyst dynamic restructuring↗
DOE OSTI2023

Mean field model parameterization to recover coverage-dependent kinetics

Lateral interactions between adsorbates introduce coverage dependence into adsorption energies and activation barriers of surface reactions. Lattice-based kinetic Monte Carlo (kMC) simulations can capture these interactions quantitatively but are laborious to parameterize and solve. Mean field models are more tractable, but protocols to construct and parameterize them are unclear. Here we explore the ability of a coverage-aware mean-field model to map to a lattice-kMC model of a generic two-step reaction network, including quasi-equilibrated adsorption and rate-limiting dissociation steps. We derive expressions for mean-field and coverage-dependent adsorption energies and dissociation barriers and parameterize against lattice-kMC predictions. We show that the parameterized mean-field rates correlate with ground truth lattice-kMC results across a wide range of reaction conditions and identify regions where the mean field fails. The mean field model similarly captures kMC-derived rate-order, Arrhenius and Sabatier plots at a greatly reduced computational cost. Further, the results provide guidance for parameterizing mean-field models, benchmarked against explicit lattice-based approaches for incorporating the influence of coverage effects.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Diffusion of NiH on Ni(111) and of Ni with Adsorbed H on Ni(111) in the Context of Ni Coarsening in Solid Oxide Cells

In the Ni-based hydrogen electrode of a solid oxide cell (SOC), Ni coarsening is an important degradation mechanism and could be enhanced by NiH diffusing on the Ni particle surfaces. Here, in this work, the average lifetime and diffusion distance of NiH on Ni(111) are computed using density-functional theory and kinetic Monte Carlo methods. It is found that NiH is extremely short-lived and, thus, cannot promote coarsening in the SOC. Also, the diffusion of Ni on Ni(111) is shown to be at most slightly accelerated by H along NiH dissociation paths involving the movement of Ni with a nearby H. Based on this result, coarsening is not likely to be dramatically accelerated by the diffusion of Ni with H on Ni(111). However, support is provided for the experimental procedure of measuring the product of surface coverage and single-species diffusivity of Ni on Ni(111) in a hydrogen-rich atmosphere.

Ni coarsening↗
DOE OSTI2020

Computational modeling of charge hopping dynamics along a disordered one-dimensional wire with energy gradients in quantum environments

This computational study investigates the effects of energy gradients on charge hopping dynamics along a one-dimensional chain of discrete sites coupled to quantum bath, which is modeled at the level of Pauli master equation (PME). This study also assesses the performance of different approximations for the hopping rates. Three different methods for solving the PME, a fourth order Runge–Kutta method, numerical diagonalization of the rate matrix followed by analytic propagation, and kinetic Monte Carlo simulation method, are tested and confirmed to produce virtually identical values of time dependent mean square displacement, diffusion constant, and mobility. Five different rate expressions, exact numerical evaluation of Fermi’s Golden Rule (FGR) rate, stationary phase interpolation (SPI) approximation, semiclassical approximation, classical Marcus rate, and Miller–Abrahams rate, are tested to help understand the effects of approximations in representing quantum environments in the presence of energy gradients. The results based on direct numerical evaluation of FGR rate exhibit transition from diffusive to non-diffusive behavior with the increase in the gradient and show that the charge transport in the quantum bath is more sensitive to the magnitude of the gradient and the disorder than in the classical bath. Among all the four approximations for the hopping rates, the SPI approximation is confirmed to work best overall. A comparison of two different methods to calculate the mobility identifies drift motion of the population distribution as the major source of non-diffusive behavior and provides more reliable information on the contribution of quantum bath.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Toward a Better Understanding of Ni Coarsening in Solid Oxide Cells: NiH on Ni (111) Examined Using a Combined Theoretical Approach

The coarsening of the Ni particles in the hydrogen electrode of solid oxide cells (SOCs) is an important degradation mechanism. Here, in this paper, density-functional theory and kinetic Monte Carlo methods are used to explore our recent hypothesis that the surface diffusion of NiH may cause faster Ni coarsening in electrolysis cell mode under an overpotential. Using both methods, the diffusion constant or diffusivity of NiH on Ni (111) is determined as the product of the surface coverage and single-molecule diffusivity for the first time considering all possible diffusion paths. It is then determined versus overpotential at the triple-phase boundary of the hydrogen electrode assuming a typical operating temperature of the SOC. Under a significant overpotential, the diffusivity of NiH is found to be sufficiently large to support the above hypothesis that NiH may promote Ni coarsening. However, based on the adsorption configurations identified, the dissociation and reformation of NiH on Ni (111) could occur. Thus, more work is needed to develop a model of Ni coarsening considering both molecular and dissociated forms of NiH.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Mineral Deposition on the Rough Walls of a Fracture

Modeling carbonate growth in fractures and pores is important for understanding carbon sequestration in the environment or when supersaturated solutions are injected into rocks. Here, we study the simple but nontrivial problem of calcite growth on fractures with rough walls of the same mineral using kinetic Monte Carlo simulations of attachment and detachment of molecules and scaling approaches. First, we consider wedge-shaped fracture walls whose upper terraces are in the same low-energy planes and show that the valleys are slowly filled by the propagation of parallel monolayer steps in the wedge sides. The growth ceases when the walls reach these low-energy configurations so that a gap between the walls may not be filled. Second, we consider fracture walls with equally separated monolayer steps (vicinal surfaces with roughness below 1 nm) and show that growth by step propagation will eventually clog the fracture gap. In both cases, scaling approaches predict the times to attain the final configurations as a function of the initial geometry and the step-propagation velocity, which is set by the saturation index. The same reasoning applied to a random wall geometry shows that step propagation leads to lateral filling of surface valleys until the wall reaches the low-energy crystalline plane that has the smallest initial density of molecules. Thus, the final configurations of the fracture walls are much more sensitive to the crystallography than to the roughness or the local curvature. The framework developed here may be used to determine those configurations, the times to reach them, and the mass of deposited mineral. Effects of transport limitations are discussed when the fracture gap is significantly narrowed.

calcite↗