An Fe‐Ni‐Cr embedded atom method potential for austenitic and ferritic systems

Zhou, Xiaowang; Foster, Michael E.; Sills, Ryan B.

doi:10.1002/jcc.25573

Cited by 86 publications

(26 citation statements)

References 40 publications

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“…Using the accelerated ML model outlined in Fig. 3, we compute GB segregation energy spectra for 18 solutes 37,40,42,53,58,[75][76][77][78][79][80][81] in a 20x20x20 nm 3 Ni polycrystal of grain size 10 nm; the anti-segregation region (ΔE seg i >0) is shaded. The spectra are fitted to the skew-normal function Eq.…”

Section: Discussionmentioning

confidence: 99%

Learning grain boundary segregation energy spectra in polycrystals

2020

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The segregation of solute atoms at grain boundaries (GBs) can profoundly impact the structural properties of metallic alloys, and induce effects that range from strengthening to embrittlement. And, though known to be anisotropic, there is a limited understanding of the variation of solute segregation tendencies across the full, multidimensional GB space, which is critically important in polycrystals where much of that space is represented. Here we develop a machine learning framework that can accurately predict the segregation tendency—quantified by the segregation enthalpy spectrum—of solute atoms at GB sites in polycrystals, based solely on the undecorated (pre-segregation) local atomic environment of such sites. We proceed to use the learning framework to scan across the alloy space, and build an extensive database of segregation energy spectra for more than 250 metal-based binary alloys. The resulting machine learning models and segregation database are key to unlocking the full potential of GB segregation as an alloy design tool, and enable the design of microstructures that maximize the useful impacts of segregation.

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Section: Discussionmentioning

confidence: 99%

Learning grain boundary segregation energy spectra in polycrystals

2020

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“…Periodic boundary conditions were applied in x-, y-, and z-dimensions. The embedded atom method (EAM), developed by Zhou et al [ 28 ], was also utilized. All polycrystalline samples were minimized using the conjugate gradient algorithm to get a steady atom configuration.…”

Section: Simulation Methodsmentioning

confidence: 99%

Molecular Dynamics as a Means to Investigate Grain Size and Strain Rate Effect on Plastic Deformation of 316 L Nanocrystalline Stainless-Steel

Husain

Hongzheng

et al. 2020

Materials

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In the present study, molecular dynamics simulations were employed to investigate the effect of strain rate on the plastic deformation mechanism of nanocrystalline 316 L stainless-steel, wherein there was an average grain of 2.5–11.5 nm at room temperature. The results showed that the critical grain size was 7.7 nm. Below critical grain size, grain boundary activation was dominant (i.e., grain boundary sliding and grain rotation). Above critical grain size, dislocation activities were dominant. There was a slight effect that occurred during the plastic deformation mechanism transition from dislocation-based plasticity to grain boundaries, as a result of the stress rate on larger grain sizes. There was also a greater sensitive on the strain rate for smaller grain sizes than the larger grain sizes. We chose samples of 316 L nanocrystalline stainless-steel with mean grain sizes of 2.5, 4.1, and 9.9 nm. The values of strain rate sensitivity were 0.19, 0.22, and 0.14, respectively. These values indicated that small grain sizes in the plastic deformation mechanism, such as grain boundary sliding and grain boundary rotation, were sensitive to strain rates bigger than those of the larger grain sizes. We found that the stacking fault was formed by partial dislocation in all samples. These stacking faults were obstacles to partial dislocation emission in more sensitive stress rates. Additionally, the results showed that mechanical properties such as yield stress and flow stress increased by increasing the strain rate.

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“…In Figure 1c, the face-centered cubic (fcc) lattice structures of the grains are indicated in green and the close-packed (cp) grain boundaries are indicated in white. The embedded atom method potential developed by Zhou et al [21] was adopted to analyze the atomic interactions in the fcc nanocrystalline austenitic stainless steel. After constructing the simulation boxes and prior to lattice, periodic boundary conditions were applied in the x, y, and z directions and the models were minimized using the conjugate gradient algorithm to obtain a stable atomic configuration.…”

Section: Simulation Methodologymentioning

confidence: 99%

Influence of Temperature on Mechanical Properties of Nanocrystalline 316L Stainless Steel Investigated via Molecular Dynamics Simulations

Husain

Hongzheng

et al. 2020

Materials

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Molecular dynamics simulations were conducted to study the mechanical properties of nanocrystalline 316L stainless steel under tensile load. The results revealed that the Young’s modulus increased with increasing grain size below the critical average grain size. Two grain size regions were identified in the plot of yield stress. In the first region, corresponding to grain sizes above 7.7 nm, the yield stress decreased with increasing grain size and the dominant deformation mechanisms were deformation twinning and extended dislocation. In the second region, corresponding to grain sizes below 7.7 nm, the yield stress decreased rapidly with decreasing grain size and the dominant deformation mechanisms were grain boundary sliding and also grain rotation. The yield strength and Young’s modulus were both found to decrease with increasing temperature, which increased the interatomic distance and thereby decreased the interatomic bonding force.

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An Fe‐Ni‐Cr embedded atom method potential for austenitic and ferritic systems

Cited by 86 publications

References 40 publications

Learning grain boundary segregation energy spectra in polycrystals

Learning grain boundary segregation energy spectra in polycrystals

Molecular Dynamics as a Means to Investigate Grain Size and Strain Rate Effect on Plastic Deformation of 316 L Nanocrystalline Stainless-Steel

Influence of Temperature on Mechanical Properties of Nanocrystalline 316L Stainless Steel Investigated via Molecular Dynamics Simulations

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