Atomistic calculation of elastic constants of alpha-iron containing point defects by means of magnetic interatomic potentials

Chiesa, S.; Derlet, P. M.; Dudarev, S. L.; Swygenhoven, H. Van

doi:10.1016/j.jnucmat.2008.12.056

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…This is also corroborated by the results obtained by ours and presented in Table 1 . Our results also agree with 38 for Fe with point defects.…”

Section: Resultssupporting

confidence: 89%

Rapid and accurate predictions of perfect and defective material properties in atomistic simulation using the power of 3D CNN-based trained artificial neural networks

Peivaste,

Ramezani,

Alahyarizadeh

et al. 2024

Sci Rep

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This article introduces an innovative approach that utilizes machine learning (ML) to address the computational challenges of accurate atomistic simulations in materials science. Focusing on the field of molecular dynamics (MD), which offers insight into material behavior at the atomic level, the study demonstrates the potential of trained artificial neural networks (tANNs) as surrogate models. These tANNs capture complex patterns from built datasets, enabling fast and accurate predictions of material properties. The article highlights the application of 3D convolutional neural networks (CNNs) to incorporate atomistic details and defects in predictions, a significant advancement compared to current 2D image-based, or descriptor-based methods. Through a dataset of atomistic structures and MD simulations, the trained 3D CNN achieves impressive accuracy, predicting material properties with a root-mean-square error below 0.65 GPa for the prediction of elastic constants and a speed-up of approximately 185 to 2100 times compared to traditional MD simulations. This breakthrough promises to expedite materials design processes and facilitate scale-bridging in materials science, offering a new perspective on addressing computational demands in atomistic simulations.

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“…This is also corroborated by the results obtained by ours and presented in Table 1 . Our results also agree with 38 for Fe with point defects.…”

Section: Resultssupporting

confidence: 89%

Rapid and accurate predictions of perfect and defective material properties in atomistic simulation using the power of 3D CNN-based trained artificial neural networks

Peivaste,

Ramezani,

Alahyarizadeh

et al. 2024

Sci Rep

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“…In the case of pure -Fe this means E 100 ¼ 141.5 GPa, E 110 ¼ 197.4 GPa and E 111 ¼ 231.6 GPa. The tendency is that E 100 < E 110 < E 111 as well as the absolute values are in good agreement with the anisotropic tensile moduli determined elsewhere [36]. However, some discrepancy can be found when E is calculated from the elastic constants in Table 6 using the formula…”

Section: Materials Stiffness -Young's Modulussupporting

confidence: 83%

Atomistic multiscale simulations on the anisotropic tensile behaviour of copper-alloyed alpha-iron at different states of thermal ageing

Molnár

Binkele

Hocker

et al. 2012

Philosophical Magazine

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The mechanical behaviour of steels is strongly related to their underlying atomistic structures which evolve during thermal treatment. Cu-alloyedFe undergoes a change in material behaviour during the ageing process, especially at temperatures of above 300 C, where precipitates form on a large time-scale within the -Fe matrix, yielding first a precipitation strengthening of the material. As the precipitates grow further in time, the material strength decreases again. This complex process is modelled with a multiscale approach, combining Kinetic Monte Carlo (KMC) with Molecular Dynamics (MD) simulations in a sequential way and exploiting the advantages of both methods while simultaneously circumventing their particular disadvantages. The formation of precipitates is modelled on a single-crystal lattice with a diffusion based KMC approach. Transferring selected precipitation states at different ageing times to MD simulations allows the performance of nano tensile tests and the analysis of failure initiation. The anisotropic tensile behaviour is investigated in the [100], [110] and [111] directions, showing monotonically decreasing tensile strengths and deformation strains. Hence precipitation strengthening is mainly due to dislocation-precipitate interactions which are non-existent at small tensile loadings in this scenario. At the point of ductile failure, dislocations are generated at the interfaces between precipitates and the Fe matrix. Straining in the [100] direction, they lie on {110} and {112} glide planes, as expected. With the method presented here, the changes of the anisotropic tensile moduli are related to different states of thermal ageing, i.e., to nucleation, growth and Ostwald ripening of Cu precipitates.

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“…Vacancies have been analytically and computationally shown to change elastic moduli on the same order of magnitude as their concentration [3,4]. Interstitial atoms are predicted to have an effect between two [3] and ten [4][5][6] times as strong. Thermal equilibrium populations of defects are found in levels from 10 −10 to 10 −4 atom fraction for vacancies from room temperature to the melting point, respectively, and at levels thousands of times lower for interstitials.…”

Section: Published By the American Physical Society Under The Terms Omentioning

confidence: 99%

Bridging the gap to mesoscale radiation materials science with transient grating spectroscopy

et al. 2016

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Direct mesoscale measurements of radiation-induced changes in the mechanical properties of bulk materials remain difficult to perform. Most widely used characterization techniques are either macro-or microscale in nature, focusing on overall properties or overly small areas for analysis. Linking the atomic structure of irradiated materials directly with their radiation-affected properties remains one of the largest unmet challenges in radiation materials science. By measuring the change in surface acoustic wave speed as a function of relative orientation on metallic single crystals, we demonstrate that transient grating (TG) spectroscopy experiments have the sensitivity necessary to detect radiation-induced material property changes. We also show that classical molecular dynamics (MD) simulations can be used to accurately simulate orientation-based changes in surface acoustic wave speed in TG experiments, by comparing with experimental measurements and theoretical predictions. The agreement between theory, simulation, and experiment gives confidence in classical MD as a predictive tool to simulate defect-based changes in elastic properties, which cannot yet be fully treated by theory. This ability is of critical importance for the informed use of TG spectroscopy to measure material property changes induced by radiation damage, which may vary by amounts formerly too small for reliable in situ detection. Finally, our MD simulation framework is used to study the effect of an imposed vacancy population on the acoustic response of several materials. The results of these studies indicate that TG experiments are well suited to the ex situ and in situ study of radiation-induced material property changes.

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Atomistic calculation of elastic constants of alpha-iron containing point defects by means of magnetic interatomic potentials

Cited by 6 publications

References 14 publications

Rapid and accurate predictions of perfect and defective material properties in atomistic simulation using the power of 3D CNN-based trained artificial neural networks

Rapid and accurate predictions of perfect and defective material properties in atomistic simulation using the power of 3D CNN-based trained artificial neural networks

Atomistic multiscale simulations on the anisotropic tensile behaviour of copper-alloyed alpha-iron at different states of thermal ageing

Bridging the gap to mesoscale radiation materials science with transient grating spectroscopy

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