Kinetic activation-relaxation technique

Béland, Laurent Karim; Brommer, P.E.; El-Mellouhi, Fedwa; Joly, Jean François; Mousseau, Normand

doi:10.1103/physreve.84.046704

Cited by 141 publications

(174 citation statements)

References 62 publications

(140 reference statements)

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“…There are other plausible methods such as ab-initio molecular dynamics 47 or potential energy surface sampling approach (e.g. kinetic activation relaxation 48,49 , and the autonomous basin climbing 50 ) can be used but the computational demands of such calculations are often prohibitive with full ab-initio methods. Therefore, in this work, we use an approach based on kinetic Monte Carlo (kMC).…”

Section: Kinetic Monte Carlo Modelmentioning

confidence: 99%

Cs diffusion in SiC high-energy grain boundaries

Szlufarska

Morgan

2017

Journal of Applied Physics

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Cesium (Cs) is a radioactive fission product whose release is of concern for Tristructural-Isotropic (TRISO) fuel particles. In this work, Cs diffusion through high energy grain boundaries (HEGBs) of cubic-SiC is studied using an ab-initio based kinetic Monte Carlo (kMC) model. The HEGB environment was modeled as an amorphous SiC (a-SiC), and Cs defect energies were calculated using density functional theory (DFT). From defect energies, it was suggested that the fastest diffusion mechanism as Cs interstitial in an amorphous SiC. The diffusion of Cs interstitial was simulated using a kMC, based on the site and transition state energies sampled from the DFT. The Cs HEGB diffusion exhibited an Arrhenius type diffusion in the range of 1200-1600°C. The comparison between HEGB results and the other studies suggests not only that the GB diffusion dominates the bulk diffusion, but also that the HEGB is one of the fastest grain boundary paths for the Cs diffusion. The diffusion coefficients in HEGB are clearly a few orders of magnitude lower than the reported diffusion coefficients from in-and out-of-pile samples, suggesting that other contributions are responsible, such as a radiation enhanced diffusion.

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Section: Kinetic Monte Carlo Modelmentioning

confidence: 99%

Cs diffusion in SiC high-energy grain boundaries

Szlufarska

Morgan

2017

Journal of Applied Physics

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“…29 This has led to the development of "basin" and "superbasin" techniques. 10,15,44 The basis of the superbasin technique is to regard connected low energy barrier states as one object-a superbasin. The pathways within the superbasin are ignored and only those that escape from the superbasin are used in the AKMC algorithm.…”

Section: Film Growth When Some Pathways Have Small Energy Barriersmentioning

confidence: 99%

“…Our implementation is an adaption of the basin-autoconstruction mean rate method (bac-MRM) 10 rather than the LS-KMC method. 44 The reason for this is that the LS-KMC method, which uses absorbing Markov chains 45 to decide an escaping transition and the simulation time increment, requires the whole superbasin to be explored.…”

Section: Film Growth When Some Pathways Have Small Energy Barriersmentioning

confidence: 99%

“…An alternative approach pioneered by Mousseau and Henkelman and Jónsson [10][11][12] is to use adaptive kinetic Monte Carlo (AKMC) by determining the barriers and rates R i , i = 1, 2 . .…”

Section: Introductionmentioning

confidence: 99%

“…In AKMC, besides the way local searches are conducted, the method includes a transition re-use mechanism [similar to that used in the kinetic activation-relaxation technique (KART) 10 ] and implements a mean rate method 15 for dealing with the low barrier problem (discussed in Sec. IV) and an external driver that allows for the inclusion of additional events in the rate tablessuch as surface deposition 34 modeled using MD.…”

Section: Introductionmentioning

confidence: 99%

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Reaction pathways in atomistic models of thin film growth

Lloyd

Zhou

et al. 2017

The Journal of Chemical Physics

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The atomistic processes that form the basis of thin film growth often involve complex multi-atom movements of atoms or groups of atoms on or close to the surface of a substrate. These transitions and their pathways are often difficult to predict in advance. By using an adaptive kinetic Monte Carlo (AKMC) approach, many complex mechanisms can be identified so that the growth processes can be understood and ultimately controlled. Here the AKMC technique is briefly described along with some special adaptions that can speed up the simulations when, for example, the transition barriers are small. Examples are given of such complex processes that occur in different material systems especially for the growth of metals and metallic oxides.

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Navigating energy landscapes for materials discovery: Integrating modeling, simulation, and machine learning

Mannan,

Bihani,

Krishnan

et al. 2024

Materials Genome Engineering Advances

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The energy landscape represents a high‐dimensional mapping of the configurational states of an atomic system with their respective energies. Under isobaric conditions, enthalpy landscapes can be used to account for volumetric changes of the system. Understanding the energy or enthalpy landscape holds the key for discovering materials with targeted properties, since the landscape encapsulates the complete thermodynamic and kinetic behavior of a system, including relaxation, metastable phases, and reactivity. However, the curse of dimensionality prohibits one from enumerating and visualizing the energy landscape—the energy landscape of an N‐atom system has 3N dimensions. Here, we outline the emerging computational techniques that allow the exploration of complex energy landscapes of materials in three distinct categories: the classical, metaheuristic, and machine learning approaches. We discuss the advantages and disadvantages associated with each of these methods, with a focus on the nature of problems where they can provide excellent solutions (and vice versa). Altogether, in addition to giving an overview of existing approaches, we hope the review provides an impetus to develop novel methods to explore the energy landscapes that can, in turn, provide both a fundamental understanding of the physics of materials and accelerate the discovery of novel materials.

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Kinetic activation-relaxation technique

Cited by 141 publications

References 62 publications

Cs diffusion in SiC high-energy grain boundaries

Cs diffusion in SiC high-energy grain boundaries

Reaction pathways in atomistic models of thin film growth

Navigating energy landscapes for materials discovery: Integrating modeling, simulation, and machine learning

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