Object Kinetic Monte Carlo calculations of electron and He irradiation of nickel

Gámez, B.; Gámez, L.; Ortiz, C.J.; Caturla, M.J.; Perlado, J.M.

doi:10.1016/j.jnucmat.2008.12.066

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…The present workflow avoids the time-consuming evaluation of system's dynamical matrix (O(N 2 )) and its spectrum (O(N 3 )). The current efficient solution opens many avenues in the field of on-the-fly exploration of complex energetic landscapes, for example using semi-automatic atomistic frameworks such as lattice or off-lattice and relaxed Kinetic Monte Carlo [11,[83][84][85][86][87][88][89]94].…”

Section: Discussionmentioning

confidence: 99%

“…The migration coefficients can be computed from the transition rates between the relevant minima of the energy landscape [13,15,[79][80][81][82]. As such, the transition rates are observable of paramount importance in the implementation of multiscale simulations such as Kinetic Monte Carlo (KMC), whatever the variant: objects [83][84][85][86][87][88][89], events [90][91][92][93], or off-lattice framework [11,94]. Each variation of the KMC method mentioned above has a special recipe for defining the physical reality corresponding to the transition rate.…”

Section: Application Of Linear Models For Harmonic Transition State T...mentioning

confidence: 99%

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Machine learning surrogate models for strain-dependent vibrational properties and migration rates of point defects

Lapointe

Swinburne

Proville

et al. 2022

Phys. Rev. Materials

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Machine learning surrogate models employing atomic environment descriptors have found wide applicability in materials science. In our previous work, this approach yielded accurate and transferable predictions of the vibrational formation entropy of point defects for O(N ) computational cost. The present study investigates the limits of data driven surrogate models in accuracy and applicability for vibrational properties. We propose an improvement of the accuracy by extending the fitting capacity of the model by increasing the dimension of the descriptor space. This is achieved by using a non-linear relation between descriptors -target observables and when it is possible by including physical relevant information of the underlying energy landscape. The non-linear extension is used to learn the formation entropy of defects with or without applied strain whilst including physical information such as the minimum-saddle point sequences are employed for the migration of point defects, a key ingredient of transition state theory rate approximations. We find excellent predictive power after augmenting the dimensionality of the descriptor space, as demonstrated on large defect databases in α-iron and amorphous silicon based on semi-empirical force fields. The current linear surrogate models are used to investigate the correlation between migration entropy and energy. Our approaches reproduce the Meyer-Neldel compensation law observed from direct calculations in amorphous Si systems. Moreover, the same abstract descriptor space representation for entropy and energy is then used for the statistical correlation analysis. For linear surrogate models, we show that the energy-entropy statistical correlations can be reinterpreted in descriptor space. This provides a simple statistical criterion for marginal interpretation of the compensation law. More generally, the present work shows how linear surrogate models can accelerate high-throughput workflows, aid the construction of mesoscale material models and provide new avenues for correlation analysis.

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Section: Discussionmentioning

confidence: 99%

Section: Application Of Linear Models For Harmonic Transition State T...mentioning

confidence: 99%

Machine learning surrogate models for strain-dependent vibrational properties and migration rates of point defects

Lapointe

Swinburne

Proville

et al. 2022

Phys. Rev. Materials

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show abstract

“…All these studies underline the complex balance between point defect and helium clustering, cluster growth, trapping and detrapping reactions that lead to different microstructure evolutions. Most of these investigations have been done in bcc Fe and W, but a few studies in fcc metals (Figure 10) and even austenitic steels are available (Caturla et al 2003) (Gámez et al 2009) (De Backer et al 2015. A preliminary study comparing the formation of mixed He vacancy clusters in bcc and fcc Fe seems even to find no major difference in clustering behavior (Suzudo et al 2010).…”

Section: Iv-4 Solute or Impurity / Gas Atoms Diffusion And The Formatmentioning

confidence: 99%

Kinetic Monte Carlo Simulations of Irradiation Effects

Becquart

Mousseau

Domain

2020

Comprehensive Nuclear Materials

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The evolution of micro defects in He + irradiated FeCrNi alloy during isochronal annealing

Cao

Jin

et al. 2015

Nuclear Instruments and Methods in Physics Research Section B:

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Object Kinetic Monte Carlo calculations of electron and He irradiation of nickel

Cited by 3 publications

References 7 publications

Machine learning surrogate models for strain-dependent vibrational properties and migration rates of point defects

Machine learning surrogate models for strain-dependent vibrational properties and migration rates of point defects

Kinetic Monte Carlo Simulations of Irradiation Effects

The evolution of micro defects in He + irradiated FeCrNi alloy during isochronal annealing

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