Atomic interaction of the MEAM type for the study of intermetallics in the Al–U alloy

Pascuet, M.I.; Fernández, J.R.

doi:10.1016/j.jnucmat.2015.09.030

Cited by 48 publications

(16 citation statements)

References 58 publications

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“…To highlight the influence of the interatomic potential on the simulation results, different EAM and MEAM basis interatomic potentials for Al and Al alloy were examined [29][30][31][32][33][34][35][36][37][38]. EAM potentials are widely used in the MD simulations for metals and their alloys.…”

Section: Simulation Methodsmentioning

confidence: 99%

Grain boundary induced deformation mechanisms in nanocrystalline Al by molecular dynamics simulation: From interatomic potential perspective

Zhang

Shibuta

Huang

et al. 2019

Computational Materials Science

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Nanocrystalline metals exhibit many excellent mechanical properties and their underlying deformation mechanisms have been studying widespread. The well-designed atomistic simulations can predict the mechanical behavior of materials ahead of experiments and provide sufficient information on the atomic scale. The choice of appropriate interatomic potential is one of the main concerns of any atomistic simulation that needs to be seriously considered in order to obtain reliable results. In this study, we investigated the mechanical response and the deformation mechanisms of nanocrystalline Al under uniaxial loading by molecular dynamics (MD) simulations with different embedded atom method (EAM) interatomic potentials. The selection of potential has a significant influence on the simulation result, and the reliability of these potentials was evaluated based on the available experimental data in the literature. In the elastic stage, the stress-strain response and Young's modulus of the simulated samples varied with different potentials. Three independent elastic constants of single crystal Al were used to predict the isotropic elastic modulus of the polycrystal Al sample. In the plastic stage, multiple grain boundary (GB) induced deformation mechanisms were observed, including GB migration, intergranular fracture, dislocation nucleation from GB, and deformation twinning. The generalized stacking fault energies of Al were calculated by MD simulation using different potentials, and their effects on the dislocation nucleation and deformation twinning mechanisms were discussed. This work signifies the important role of GBs play in nanocrystalline metals during plastic deformation and highlights the significance of using the appropriate interatomic potential for a specific simulation problem.Abstract: Nanocrystalline metals exhibit many excellent mechanical properties and their underlying deformation mechanisms have been studying widespread. The well-designed atomistic simulations can predict the mechanical behavior of materials ahead of experiments and provide sufficient information on the atomic scale. The choice of appropriate interatomic potential is one of the main concerns of any atomistic simulation that needs to be seriously considered in order to obtain reliable results. In this study, we investigated the mechanical response and the deformation mechanisms of nanocrystalline Al under uniaxial loading by molecular dynamics (MD) simulations with different embedded atom method (EAM) interatomic potentials. The selection of potential has a significant influence on the simulation result, and the reliability of these potentials was evaluated based on the available experimental data in the literature. In the elastic stage, the stress-strain response and Young's modulus of the simulated samples varied with different potentials. Three independent elastic constants of single crystal Al were used to predict the isotropic elastic modulus of the polycrystal Al sample. In the plastic stage, multiple grain boundary (GB) induced de...

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Section: Simulation Methodsmentioning

confidence: 99%

Grain boundary induced deformation mechanisms in nanocrystalline Al by molecular dynamics simulation: From interatomic potential perspective

Zhang

Shibuta

Huang

et al. 2019

Computational Materials Science

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“…For verifying the accuracy of potential parameter set of “ A = 17 500 (eV), ρ = 0.25 (Å), and C = 173.0115 (eV‐Å 6 )” [the parameter C is calculated by Equation ], structural property of mass density ( ρ m ); thermodynamic properties including linear thermal expansion coefficient ( α L ), constant‐pressure specific heat capacity ( c p ), and melting temperature ( T m ); mechanical properties of Young's modulus along 〈100〉 orientation ( E 〈100〉 ) and ultimate tensile strength ( σ u ), are calculated as shown in Table (Detailed simulation and calculation methodologies for various properties are elaborated in Section 5 of “Simulation and Calculation Methodology of Various Properties for Verification”). Except T m , all other properties are measured at temperature of 300 K. The RMSE of the properties calculated by each potential, including Buckingham (BUCK) potential developed in this work, modified embedded atom method (MEAM) potential, embedded atom method (EAM) potential, and angular dependent potential (ADP) are shown in the last column in Table . The α L and c p are calculated from the centered finite difference method, the T m is determined from the volume‐temperature curve ( Figure 5a), and the E 〈100〉 and σ u is obtained from the strain–stress curve (Figure 5b).…”

Section: Resultsmentioning

confidence: 99%

Interatomic Potential Model Development: Finite‐Temperature Dynamics Machine Learning

Wang

Shin

Lee

2019

Advcd Theory and Sims

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Developing an accurate interatomic potential model is a prerequisite for achieving reliable results from classical molecular dynamics (CMD) simulations; however, most of the potentials are biased as specific simulation purposes or conditions are considered in the parameterization. For developing an unbiased potential, a finite‐temperature dynamics machine learning (FTD‐ML) approach is proposed, and its processes and feasibility are demonstrated using the Buckingham potential model and aluminum (Al) as an example. Compared with conventional machine learning approaches, FTD‐ML exhibits three distinguished features: 1) FTD‐ML intrinsically incorporates more extensive configurational and conditional space for enhancing the transferability of developed potentials; 2) FTD‐ML employs various properties calculated directly from CMD, for ML model training and prediction validation against experimental data instead of first‐principles data; 3) FTD‐ML is much more computationally cost effective than first‐principles simulations, especially when the system size increases over 103 atoms as employed in this research for ensuring reliable training data. The Al Buckingham potential developed by the FTD‐ML approach exhibits good performance for general simulation purposes. Thus, the FTD‐ML approach is expected to contribute to a fast development of interatomic potential model suitable for various simulation purposes and conditions, without limitation of model type, while maintaining experimental‐level accuracy.

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“…Subsequently, the matrix-vector form of the elastic stiffness tensor can be used to determine the elastic modulus of the solid particle. The result is given in Tables 2 and 3 for the aluminum and zinc, respectively, in comparison with existing literature where elastic stiffness tensor is determined with alternative means including experimental, first principles and simulation using molecular dynamics [53][54][55][56][57] . The stiffness tensor defined using the combination of microporomechanics theory and experimental nanoindentation measurement is found to be within a reasonable range.…”

Section: Statistical Deconvolution Outcome Using Statistical Deconvomentioning

confidence: 99%

Transversely isotropic elastic-plastic properties in thermal arc sprayed Al–Zn coating: a microporomechanics approach

Huen

Lee

Vimonsatit

et al. 2020

Sci Rep

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Atomic interaction of the MEAM type for the study of intermetallics in the Al–U alloy

Cited by 48 publications

References 58 publications

Grain boundary induced deformation mechanisms in nanocrystalline Al by molecular dynamics simulation: From interatomic potential perspective

Grain boundary induced deformation mechanisms in nanocrystalline Al by molecular dynamics simulation: From interatomic potential perspective

Interatomic Potential Model Development: Finite‐Temperature Dynamics Machine Learning

Transversely isotropic elastic-plastic properties in thermal arc sprayed Al–Zn coating: a microporomechanics approach

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