EAM potential (LAMMPS cubic hermite tabulation) for Fe-Ni-Cr developed by Zhou, Foster and Sills (2018) v000

Xiaowang Zhou; Michael E. Foster; Ryan B Sills

doi:10.25950/e28aaa47

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EAM_Dynamo_ZhouFosterSills_2018_FeNiCr__MO_036303866285_000

Interatomic potential for Chromium (Cr), Iron (Fe), Nickel (Ni).
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Title A single sentence description.	EAM potential (LAMMPS cubic hermite tabulation) for Fe-Ni-Cr developed by Zhou, Foster and Sills (2018) v000
Description A short description of the Model describing its key features including for example: type of model (pair potential, 3-body potential, EAM, etc.), modeled species (Ac, Ag, ..., Zr), intended purpose, origin, and so on.	Developed using density functional theory as a guide, this potential has been demonstrated to enable stable molecular dynamics simulations of stainless‐steel alloys at high temperatures, accurately reproduce the stacking fault energy-known to strongly influence the mode of plastic deformation (e.g., twinning vs. dislocation glide vs. cross‐slip)-of these alloys over a range of compositions, and give reasonable elastic constants, energies, and volumes for various compositions. The latter are pertinent for determining short‐range order and solute strengthening effects. These results suggest that this potential is suitable for studying mechanical properties of austenitic and ferritic stainless‐steels which have vast implementation in the scientific and industrial communities.
Species The supported atomic species.	Cr, Fe, Ni
Disclaimer A statement of applicability provided by the contributor, informing users of the intended use of this KIM Item.	None
Content Origin	https://www.ctcms.nist.gov/potentials/entry/2018--Zhou-X-W-Foster-M-E-Sills-R-B--Fe-Ni-Cr/

Contributor	I Nikiforov
Maintainer	I Nikiforov
Developer	Xiaowang Zhou Michael E. Foster Ryan B Sills
Published on KIM	2022
How to Cite	This Model originally published in [1] is archived in OpenKIM [2-5]. [1] Zhou XW, Foster ME, Sills RB. An Fe-Ni-Cr embedded atom method potential for austenitic and ferritic systems. Journal of Computational Chemistry [Internet]. 2018;39(29):2420–31. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.25573 doi:10.1002/jcc.25573 — (Primary Source) A primary source is a reference directly related to the item documenting its development, as opposed to other sources that are provided as background information. [2] Zhou X, Foster ME, Sills RB. EAM potential (LAMMPS cubic hermite tabulation) for Fe-Ni-Cr developed by Zhou, Foster and Sills (2018) v000. OpenKIM; 2022. doi:10.25950/e28aaa47 [3] Foiles SM, Baskes MI, Daw MS, Plimpton SJ. EAM Model Driver for tabulated potentials with cubic Hermite spline interpolation as used in LAMMPS v005. OpenKIM; 2018. doi:10.25950/68defa36 [4] Tadmor EB, Elliott RS, Sethna JP, Miller RE, Becker CA. The potential of atomistic simulations and the Knowledgebase of Interatomic Models. JOM. 2011;63(7):17. doi:10.1007/s11837-011-0102-6 [5] Elliott RS, Tadmor EB. Knowledgebase of Interatomic Models (KIM) Application Programming Interface (API). OpenKIM; 2011. doi:10.25950/ff8f563a Click here to download the above citation in BibTeX format.
Citations This panel presents information regarding the papers that have cited the interatomic potential (IP) whose page you are on. The OpenKIM machine learning based Deep Citation framework is used to determine whether the citing article actually used the IP in computations (denoted by "USED") or only provides it as a background citation (denoted by "NOT USED"). For more details on Deep Citation and how to work with this panel, click the documentation link at the top of the panel. The word cloud to the right is generated from the abstracts of IP principle source(s) (given below in "How to Cite") and the citing articles that were determined to have used the IP in order to provide users with a quick sense of the types of physical phenomena to which this IP is applied. The bar chart shows the number of articles that cited the IP per year. Each bar is divided into green (articles that USED the IP) and blue (articles that did NOT USE the IP). Users are encouraged to correct Deep Citation errors in determination by clicking the speech icon next to a citing article and providing updated information. This will be integrated into the next Deep Citation learning cycle, which occurs on a regular basis. OpenKIM acknowledges the support of the Allen Institute for AI through the Semantic Scholar project for providing citation information and full text of articles when available, which are used to train the Deep Citation ML algorithm.	This panel provides information on past usage of this interatomic potential (IP) powered by the OpenKIM Deep Citation framework. The word cloud indicates typical applications of the potential. The bar chart shows citations per year of this IP (bars are divided into articles that used the IP (green) and those that did not (blue)). The complete list of articles that cited this IP is provided below along with the Deep Citation determination on usage. See the Deep Citation documentation for more information. 64 Citations (58 used) Show Model Used Show All Help us to determine which of the papers that cite this potential actually used it to perform calculations. If you know, click the . M. Wagih, P. M. Larsen, and C. Schuh, “Learning grain boundary segregation energy spectra in polycrystals,” Nature Communications. 2020. link Times cited: 62 G. P’eterffy, P. D. Isp’anovity, M. E. Foster, X. W. Zhou, and R. Sills, “Length scales and scale-free dynamics of dislocations in dense solid solutions,” Materials Theory. 2020. link Times cited: 12 S. Hayakawa and H. Xu, “Development of an interatomic potential for L12 precipitates in Fe–Ni–Al alloys,” Computational Materials Science. 2024. link Times cited: 0 S. Assouli et al., “Exploring the impact of cooling rates and pressure on fragility and structural transformations in iron monatomic metallic glasses: Insights from molecular dynamics simulations,” Journal of Non-Crystalline Solids. 2023. link Times cited: 3 C. Dong and W. Jiang, “Molecular dynamics study on the effect of grain size on the plastic deformation of Fe-Cr-Ni alloy nanopolycrystals,” Materials Today Communications. 2023. link Times cited: 0 S. Biswal, R. Barik, S. Neogy, R. Tewari, A. Dutta, and D. Chakrabarti, “Unravelling the mechanisms of copper precipitation-induced strengthening in austenitic stainless steels: An atomistic approach,” Materialia. 2023. link Times cited: 0 Z. Hao, J. Hu, Y. Fan, L. Li, and L. Kong, “Research on the dynamic plasticity mechanism of additive manufactured nickel–chromium–molybdenum corrosion-resistant alloy steel under impact load,” Engineering Failure Analysis. 2023. link Times cited: 0 S. Kumar et al., “Atomistic characterization of multi nano-crystal formation process in Fe–Cr–Ni alloy during directional solidification: Perspective to the additive manufacturing,” Materials Chemistry and Physics. 2023. link Times cited: 2 T. Kaloni, A. Prudil, D. Spearot, and E. Torres, “Modeling of dislocation properties in Fe40Cr25Ni35 and Fe50Cr20Ni30 systems,” Nuclear Engineering and Design. 2023. link Times cited: 0 A. Kumar, R. Kumar, S. Kumar, and A. K. V, “Atomistic structural transformation of Fe-Cr-Ni single crystal alloy under uniaxial deformation using molecular dynamics simulation,” Materials Today: Proceedings. 2023. link Times cited: 0 W. Guo, Q.-X. Bai, Y. Dou, T. Wang, and H. Wang, “Effect of stainless-steel substrate grain boundaries on surface graphene morphology and nano-friction behavior,” Applied Surface Science. 2023. link Times cited: 0 G. Cheng et al., “Probing the effects of hydrogen on the materials used for large-scale transport of hydrogen through multi-scale simulations,” Renewable and Sustainable Energy Reviews. 2023. link Times cited: 0 J. Li, S. Yang, L. Dong, J. Zhang, Z. Zheng, and J. Liu, “Effect of crystal orientation on the nanoindentation deformation behavior of TiN coating based on molecular dynamics,” Surface and Coatings Technology. 2023. link Times cited: 0 X.-T. Li, X. Tang, Y.-F. Guo, H.-yu Li, and Y. Fan, “Modulating grain boundary-mediated plasticity of high-entropy alloys via chemo-mechanical coupling,” Acta Materialia. 2023. link Times cited: 1 C. H. Pham, H. Lorenzana, J. Belof, and N. Goldman, “Hugoniot properties of porous stainless steel: Insights from molecular dynamics simulations,” Journal of Applied Physics. 2023. link Times cited: 0 Abstract: We present a systematic study of Hugoniot properties of poro… read more Abstract: We present a systematic study of Hugoniot properties of porous 316L stainless steel using both a simple interpolation scheme and direct shock simulations in order to probe pore collapse kinetics as well as final thermodynamic states. Both methods indicate that equilibrated Hugoniot properties depend on pore density only and not on the pore distribution or size. We then create a simple porous equation of state model that is shown to be accurate for a range of validation data. This allows us to extend our simulations to make direct comparison to experimental data that have generally significantly larger system sizes and durations. In addition, our direct shock simulations indicate that the relaxation time after hotspot formation is system size dependent and can reach nanosecond timescales for the largest pores investigated in our study, thereby possibly having a measurable effect on fast dynamic loading experiments read less A. Korchuganov, D. Kryzhevich, and K. Zolnikov, “Fracture of Fe95Ni5 Alloys with Gradient-Grained Structure under Uniaxial Tension,” Metals. 2023. link Times cited: 0 Abstract: The fracture behavior of single- (fcc) and two-phase (fcc + … read more Abstract: The fracture behavior of single- (fcc) and two-phase (fcc + bcc) Fe95Ni5 samples with gradient-grained structure, under uniaxial tension, was analyzed via molecular dynamics simulation. The study revealed that fracture initiation and propagation is always associated with grain boundaries. The fracture process develops in three stages. In the first stage, nanopores are formed in the boundaries of coarse grains. The total volume of nanopores at this stage increases slowly due to the formation of new nanopores. The second stage is characterized by a rapid increase in the total nanopore volume due to the formation of nanopores, their growth along the grain boundaries, and their coalescence. At the third stage, the total nanopore volume increases linearly with deformation due to the growth of the largest nanopores. Fracture of two-phase samples begins at higher strains compared to a single-phase sample. With an increase in the volume fraction of bcc lamellae in the original sample, the number of nanopores at the third stage of fracture decreases and tends to one. read less W. Guo, Q.-X. Bai, Y. Dou, H. Wang, and S. Chen, “Investigation of the effect of stainless-steel grain size on the continuity of graphene frictional behavior using molecular dynamics (MD) simulation,” Computational Materials Science. 2023. link Times cited: 0 W. Guo, Q.-X. Bai, Y. Dou, H. Wang, and S. Chen, “Molecular Dynamics Study on the Effects of Substrate Grain Boundaries on the Adsorption State of Graphene: Implications for Nanoscale Lubrication,” ACS Applied Nano Materials. 2023. link Times cited: 2 “Influence of reciprocating friction on friction and wear characteristics of MoS2 films,” Results in Engineering. 2023. link Times cited: 3 X. W. Zhou, M. E. Foster, and R. Sills, “Enabling molecular dynamics simulations of helium bubble formation in tritium-containing austenitic stainless steels: An Fe-Ni-Cr-H-He potential,” Journal of Nuclear Materials. 2023. link Times cited: 1 Z.-tao Li et al., “Achieving superb strength in single-phase FCC alloys via maximizing volume misfit,” Materials Today. 2023. link Times cited: 8 P. Simonnin, D. Schreiber, B. Uberuaga, and K. Rosso, “Atomic Diffusion, Segregation, and Grain Boundary Migration in Nickel-Based Alloys from Molecular Dynamics Simulations,” SSRN Electronic Journal. 2023. link Times cited: 4 B. Wang, Q. Wang, R. Luo, Q. Kan, and B. Gu, “Atomistic study on high temperature creep of nanocrystalline 316L austenitic stainless steels,” Acta Mechanica Sinica. 2023. link Times cited: 1 S. Zhang, H. Zhang, H. Zhang, X. Zhao, and Y. Li, “Study on Diffusion Kinetics and Law of Chromium on the Surface of Low-Carbon Steel,” Coatings. 2023. link Times cited: 2 Abstract: Cr/low-carbon steel surface composites were prepared by aque… read more Abstract: Cr/low-carbon steel surface composites were prepared by aqueous solution co-deposition and high-temperature solid-state diffusion technology, and the macro rule of the solid-state diffusion of chromium on the surface of low-carbon steel was analyzed. The molecular dynamics (MD) method was used to simulate and calculate the diffusion process of the Cr/Fe interface, and the macro and micro diffusion mechanisms were analyzed. The results show that the diffusion of the chromium in iron is the combined action of the temperature, crystal structure and lattice distortion, and the diffusion coefficients of chromium in α-Fe and γ-Fe have little difference. The vacancy diffusion mechanism of the first adjacent transition is the main diffusion mode. In practice, chromium atoms diffuse along the grain boundaries of the low-carbon steel matrix and provide pinning at the grain boundaries to prevent grain growth. The simulation law is in good agreement with the experimental law. The variation law of the average diffusion coefficient of chromium atoms with temperature is obtained. The diffusion rate of chromium in the bcc crystal structure is obviously higher than that in the fcc crystal structure. In the same crystal structure, the diffusion coefficient of chromium increases with the increase in temperature. However, in the lattice transition temperature region, the diffusion coefficient of chromium gradually decreases with the increase in temperature until the end of the transformation. read less L. Yang, H. Wang, M. Chi, X.-guo Zeng, Y. Wang, and P. Zhao, “Molecular Dynamics Study on Hugoniot State and Mie–Grüneisen Equation of State of 316 Stainless Steel for Hydrogen Storage Tank,” Materials. 2023. link Times cited: 0 Abstract: To promote the popularization and development of hydrogen en… read more Abstract: To promote the popularization and development of hydrogen energy, a micro-simulation approach was developed to determine the Mie–Grüneisen EOS of 316 stainless steel for a hydrogen storage tank in the Hugoniot state. Based on the combination of the multi-scale shock technique (MSST) and molecular dynamics (MD) simulations, a series of shock waves at the velocity of 6–11 km/s were applied to the single-crystal (SC) and polycrystalline (PC) 316 stainless steel model, and the Hugoniot data were obtained. The accuracy of the EAM potential for Fe–Ni–Cr was verified. Furthermore, Hugoniot curve, cold curve, Grüneisen coefficient (γ), and the Mie–Grüneisen EOS were discussed. In the internal pressure energy-specific volume (P-E-V) three-dimensional surfaces, the Mie–Grüneisen EOSs show concave characteristics. The maximum error of the calculation results of SC and PC is about 10%. The results for the calculation deviations of each physical quantity of the SC and PC 316 stainless steel indicate that the grain effect of 316 stainless steel is weak under intense dynamic loads, and the impact of the grains in the cold state increases with the increase in the volume compression ratio. read less K. Peng, H. Huang, H.-F. Xu, Y. Kong, L. Zhu, and Z. Liu, “A molecular dynamics study of laser melting of densely packed stainless steel powders,” International Journal of Mechanical Sciences. 2022. link Times cited: 1 X. W. Zhou, R. Skelton, R. Sills, and C. S. Marchi, “Slip transmission and voiding during slip band Intersections in Fe70Ni10Cr20 stainless steel,” Scripta Materialia. 2022. link Times cited: 0 A.-L. Zhou, Z. J. Bai, H. Hou, Y.-L. Han, and J. Mei, “Atomic simulations of nanoscale friction behavior in polycrystalline alloy 690,” Materials Research Express. 2022. link Times cited: 1 Abstract: Fretting wear is one of the most important failure forms of … read more Abstract: Fretting wear is one of the most important failure forms of alloy 690 heat exchanger tubes in nuclear power plants. The key to understanding the fretting wear of alloys lies in the friction process, especially at the atomic scale. In this study, molecular dynamics simulations were performed on alloy 690 to investigate the nanoscale friction behavior and its influencing factors, laying a foundation for further understanding the fretting wear mechanism of alloy 690. The friction processes of a single-asperity (probe) on a smooth polycrystalline surface (matrix) were investigated by molecular dynamics simulations at the atomic scale, and the variation law of friction force during the friction process was calculated. The factors that affected the friction force were discussed, including the pressing depth, temperature, and sliding speed of the probe, and the friction force was positively correlated with the pressing depth and sliding speed of the probe, while the temperature had little effect on the friction force. Observations of the generation and evolution of dislocations during the friction process and related factors such as grinding grooves and wear debris were also reported. read less T.-X. Bui, T. Fang, and C.-I. Lee, “Investigation of the deformation behavior and mechanical characteristics of polycrystalline chromium–nickel alloys using molecular dynamics,” Journal of Molecular Modeling. 2022. link Times cited: 2 A. Korchuganov, D. Kryzhevich, and K. Zolnikov, “Deformation Behavior of Two-Phase Gradient Nanograined Fe95Ni5 Alloys under Different Types of Loading,” Metals. 2022. link Times cited: 1 Abstract: In this paper, we used molecular dynamics simulations to stu… read more Abstract: In this paper, we used molecular dynamics simulations to study the atomic mechanisms of phase transformations, plasticity features, and mechanical properties of two-phase Fe95Ni5 (at. %) samples with a gradient nanograined structure under uniaxial deformation and shear. The simulated samples with a uniform distribution of Ni atoms are composed of fcc grains from 10 to 30 nm in size, which in turn contain bcc interlayers in the form of lamellae of various distribution and size. It was shown that uniaxial loading or shear causes the bcc-fcc phase transformation in the lamellae. In the vast majority of cases, phase transformations are initiated at the junction of lamellae and grain boundaries. Deformation-induced phase transformations in lamellae occur at the front of bands propagating from grain boundaries. Grains larger than ~15 nm can have several bands or regions with differently orientated fcc lattices, whose meeting results in grain fragmentation. It was found that the atomic volume increases abruptly during the bcc-fcc structural phase transformation. The Kurdyumov–Sachs orientation relation is valid between the initial bcc and formed fcc structures. It was shown that the volume fraction and spatial distribution of the bcc phase significantly affect the yield stress of the sample. The yield stress can be increased by forming the bcc phase only in large-grained layers. This behavior is associated with the fragmentation of large grains, and consequently with grain refinement, which, in accordance with the Hall–Petch relation, improves the strength of the material. read less C.-G. Li, Z. Luo, Z.-an Tian, and K. Dong, “The concealed solid-solid structural phase transition of Fe70Ni10Cr20 under high pressure,” Materials Today Communications. 2022. link Times cited: 0 M. E. Fernandez, R. Dingreville, and D. Spearot, “Statistical perspective on embrittling potency for intergranular fracture,” Physical Review Materials. 2022. link Times cited: 3 W. Guo, Q.-X. Bai, Y. Dou, S. Chen, and H. Wang, “Molecular dynamics simulation of frictional strengthening behavior of graphene on stainless steel substrate,” Carbon. 2022. link Times cited: 6 E. Zarkadoula, Y. Yang, A. Borisevich, and E. George, “Effects of precipitate size and spacing on deformation-induced fcc to bcc phase transformation,” Materials Research Letters. 2022. link Times cited: 3 Abstract: Molecular dynamics simulations were used to study deformatio… read more Abstract: Molecular dynamics simulations were used to study deformation-induced face-centered cubic (fcc) to body-centered cubic (bcc) transformation during uniaxial compression of an 80Fe-20Ni (at%) alloy with and without precipitates. Our purpose was to better understand recent experimental results in an Fe-Ni-based medium-entropy alloy where certain precipitates were found to constrain the fcc to bcc transformation. We find that larger precipitates and smaller spacings between precipitates hinder the phase transformation by impeding relaxation of internal elastic strains. These results deconvolute the individual effects of precipitate size and spacing and help interpret the experimental results where only their combined effects could be measured. GRAPHICAL ABSTRACT IMPACT STATEMENT Using atomistic modelling, we investigate the individual effects of precipitate size and spacing in phase transition and help interpret the experimental results where only their combined effects could be measured. read less S. Paul, D. Schwen, M. Short, A. Erickson, and K. Momeni, “Effect of Differently Oriented Interlayer Phases on the Radiation Damage of Inconel-Ni Multimetallic Layered Composite,” Journal of Alloys and Compounds. 2022. link Times cited: 2 A. Korchuganov, K. Zolnikov, and D. Kryzhevich, “The Contribution of Various Plasticity Mechanisms to the Deformation Behavior of Gradient Nanograined FeNi Alloy,” Metals. 2022. link Times cited: 3 Abstract: This paper investigates the deformation behavior of a gradie… read more Abstract: This paper investigates the deformation behavior of a gradient grained FeNi sample under uniaxial tension using molecular dynamics simulations. The simulated sample consists of five layers with grains of the same size in each layer ranging from 10 to 30 nm. It is shown that the sample plasticity develops through sequential activation of different mechanisms. These are either the generation of certain structural defects, or grain boundary migration, or grain boundary sliding. The onset of plasticity is provided by partial dislocations that produce stacking faults in large grains. Other mechanisms involved in plastic deformation are the nucleation of trailing/full dislocations and twinning, which gradually affect smaller and smaller grains. Grain boundary sliding is more intensive in smallest grains due to their less constraint. Grain boundary migration generally leads to the growth of large grains. At strains below 7.0%, plasticity is mainly contributed by the evolution of stacking faults. At higher strains, the main plasticity mechanisms are twinning and grain boundary migration. As the strain increases, the maximum values of accumulated shear, the density of intragranular defects, and the number of atoms involved in intergranular rearrangements are observed first in large, then in medium, and finally in small grains. read less A. Mahata and M. Kivy, “Computational study of nanoscale mechanical properties of Fe–Cr–Ni alloy,” Molecular Simulation. 2022. link Times cited: 1 Abstract: ABSTRACT Mechanical properties of Fe–Cr–Ni alloy nanowires h… read more Abstract: ABSTRACT Mechanical properties of Fe–Cr–Ni alloy nanowires have been investigated using molecular dynamics simulation with embedded atom method and first principles approach. Various cases of uniaxial tension, compression and shear deformations have been performed and studied in this work. From the first principles calculations, the higher magnitudes of uniaxial and shear deformations resulted in higher probability of martensitic transformations. Before the first yielding, nanowires preserved the elastic stage and then the mechanical deformation proceeded in alternating quasi-elastic and yielding stages. The plastic behaviour was not observed in compression while both tensile and shear deformations showed apparent plastic behaviour. In shear deformation, due to the martensitic phase transformation, the plastic behaviour persisted for total strain of 0.6 which was much larger than that during tensile and compression. This validated the previous experimental observations. In the studied Fe–Cr–Ni nanowires, deformations were controlled by dislocations. Dislocation-mediated twinnings were captured by common neighbour analysis. Twin quantification showed that the twin activity increased with increasing strain rate. Twinnings originated from stacking faults led by 1/6 <112> Shockley partial dislocations. At elevated temperature (beyond 500 K), the materials softening happened, and 316L nanowire became more plastic under a lower stress. read less K. Chu, A. Diaz, Y. Chen, T. Zhu, and D. McDowell, “Multiscale Concurrent Atomistic-Continuum (CAC) modeling of multicomponent alloys,” Computational Materials Science. 2022. link Times cited: 5 S. Paul, M. Muralles, D. Schwen, M. Short, and K. Momeni, “A Modified Embedded-Atom Potential for Fe-Cr-Si Alloys,” The Journal of Physical Chemistry C. 2021. link Times cited: 5 Y. Liu et al., “Tuning strength-ductility combination on selective laser melted 316L stainless steel through gradient heterogeneous structure,” Additive Manufacturing. 2021. link Times cited: 29 X. W. Zhou et al., “An Fe–Ni–Cr–H interatomic potential and predictions of hydrogen-affected stacking fault energies in austenitic stainless steels,” International Journal of Hydrogen Energy. 2021. link Times cited: 18 Y.-C. Yang, C. Liu, C.-Y. Lin, and Z. Xia, “The effect of local atomic configuration in high-entropy alloys on the dislocation behaviors and mechanical properties,” Materials Science and Engineering A-structural Materials Properties Microstructure and Processing. 2021. link Times cited: 18 Z. Luo, Z.-an Tian, Y.-chao Liang, and Q. Xie, “Crystallization behavior of Fe70Ni10Cr20 during rapid solidification under different cooling rates,” Materials today communications. 2021. link Times cited: 6 M. Bahramyan, R. T. Mousavian, J. Carton, and D. Brabazon, “Nano-scale simulation of directional solidification in TWIP stainless steels: A focus on plastic deformation mechanisms,” Materials Science and Engineering: A. 2021. link Times cited: 4 S. Gowthaman and T. Jagadeesha, “Influence of temperature and strain rate on the characteristics of FeNi precipitate through molecular dynamics simulation,” Materials Today: Proceedings. 2021. link Times cited: 2 P. Simonnin, D. Schreiber, and K. Rosso, “Predicting the temperature dependence of self-diffusion behavior in Ni-Cr alloys via molecular dynamics,” Materials today communications. 2021. link Times cited: 6 S. Gowthaman and T. Jagadeesha, “Analysis on the effect of temperature o the Ni2Al3 precipitate tensile characteristics through molecular dynamics simulation,” Materials Today: Proceedings. 2021. link Times cited: 1 A. Korchuganov, D. Kryzhevich, Y. Chumakov, and A. S. Grigoriev, “Influence of grain size on the nucleation and development of plasticity in nanocrystalline FeNi films,” IOP Conference Series: Materials Science and Engineering. 2021. link Times cited: 0 Abstract: A molecular dynamics study of the features of the nucleation… read more Abstract: A molecular dynamics study of the features of the nucleation and evolution of plastic deformation in nanosized nanocrystalline FeNi films under uniform uniaxial tension is carried out. The dependences of the strength properties of the films on the grain size are obtained. It is found that the dependences of stresses at which defects begin to nucleate, maximum strength is achieved and plastic flow is realized, on the grain size have a pronounced maximum. The optimal grain structure for which the nanocrystalline film has the maximum strength at uniaxial stretching is determined. The calculated dependences allow determining the critical grain size at which the Hall-Petch relationship is reversed. It is shown that the nucleation and initial development of plasticity in nanosized films with grain size above the critical one has a dislocation nature. The twinning contribution to the film plasticity increases at strains corresponding to plastic flow. read less A. Korchuganov, D. Kryzhevich, Y. Chumakov, and A. S. Grigoriev, “Peculiarities of the development of plastic deformation in a textured FeNi alloy with a gradient grain structure,” IOP Conference Series: Materials Science and Engineering. 2021. link Times cited: 0 Abstract: The mechanisms of nucleation and development of plastic defo… read more Abstract: The mechanisms of nucleation and development of plastic deformation in the FeNi alloy with a gradient grain structure with a pronounced axial texture were revealed. The study was carried out on the basis of a molecular dynamics method. The simulated sample contained four layers, each of which consisted of grains of approximately the same size. The sample was subjected to uniform uniaxial tension at a constant rate in the direction of the grain misorientation axis. It was found that initially plastic deformation develops only in the layer with the largest grains and represents an abrupt growth of stacking faults. This process ends with the hardening of the deformed sample. At higher deformations, smaller grains contribute to the development of plasticity. At the same time, an almost ideal crystal structure can be restored in large grains, and twinning occurs in medium-sized ones. read less S. Paul, D. Schwen, M. Short, and K. Momeni, “Effect of Irradiation on Ni-Inconel/Incoloy Heterostructures in Multimetallic Layered Composites,” Journal of Nuclear Materials. 2021. link Times cited: 12 R. Sills, M. E. Foster, and X. W. Zhou, “Line-length-dependent dislocation mobilities in an FCC stainless steel alloy,” International Journal of Plasticity. 2020. link Times cited: 21 A. Husain, P. La, Y. Hongzheng, and S. Jie, “Molecular Dynamics as a Means to Investigate Grain Size and Strain Rate Effect on Plastic Deformation of 316 L Nanocrystalline Stainless-Steel,” Materials. 2020. link Times cited: 8 Abstract: In the present study, molecular dynamics simulations were em… read more Abstract: 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. read less A. Husain, P. La, Y. Hongzheng, and S. Jie, “Influence of Temperature on Mechanical Properties of Nanocrystalline 316L Stainless Steel Investigated via Molecular Dynamics Simulations,” Materials. 2020. link Times cited: 8 Abstract: Molecular dynamics simulations were conducted to study the m… read more Abstract: 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. read less I. C. Njifon and E. Torres, “Cumulative effects of primary radiation damage in alloy 800H: An atomistic simulation study,” Journal of Nuclear Materials. 2023. link Times cited: 0 R. Sills, M. E. Foster, and X. W. Zhou, “Line tension induced character angle dependence of dislocation mobility in FCC alloys,” Scripta Materialia. 2022. link Times cited: 3 A. Korchuganov, D. Kryzhevich, Y. Chumakov, and A. Grigoriev, “Deformation behavior of FeNi nanofilms with a gradient grain structure during uniaxial compression,” PROCEEDINGS OF THE INTERNATIONAL CONFERENCE “PHYSICAL MESOMECHANICS. MATERIALS WITH MULTILEVEL HIERARCHICAL STRUCTURE AND INTELLIGENT MANUFACTURING TECHNOLOGY.” 2022. link Times cited: 0 A. Korchuganov, D. Kryzhevich, Y. Chumakov, and A. Grigoriev, “Effect of grain shape on mechanical and structural response of FeNi alloy with grain size gradient under tension,” PROCEEDINGS OF THE INTERNATIONAL CONFERENCE “PHYSICAL MESOMECHANICS. MATERIALS WITH MULTILEVEL HIERARCHICAL STRUCTURE AND INTELLIGENT MANUFACTURING TECHNOLOGY.” 2022. link Times cited: 0 G. Ackland and G. Bonny, “Interatomic Potential Development,” Comprehensive Nuclear Materials. 2020. link Times cited: 4 K. Chu et al., “Investigation of chemical short range order strengthening in a model Fe–12Ni–18Cr (at. %) stainless steel alloy: A modeling and experimental study,” Acta Materialia. 2023. link Times cited: 0 Y. Lei et al., “An Embedded-Atom Method Potential for studying the properties of Fe-Pb solid-liquid interface,” Journal of Nuclear Materials. 2022. link Times cited: 1 M. Kim, J. Ha, Y. T. Kim, and J. Choi, “Stainless steel: A high potential material for green electrochemical energy storage and conversion,” Chemical Engineering Journal. 2022. link Times cited: 14 B. Waters, D. S. Karls, I. Nikiforov, R. Elliott, E. Tadmor, and B. Runnels, “Automated determination of grain boundary energy and potential-dependence using the OpenKIM framework,” Computational Materials Science. 2022. link Times cited: 5 L. Xu, L. Casillas-Trujillo, Y. Gao, and H. Xu, “Compositional effects on stacking fault energies in Ni-based alloys using first-principles and atomistic simulations,” Computational Materials Science. 2021. link Times cited: 5 K. Chu, M. E. Foster, R. Sills, X. W. Zhou, T. Zhu, and D. McDowell, “Temperature and composition dependent screw dislocation mobility in austenitic stainless steels from large-scale molecular dynamics,” npj Computational Materials. 2020. link Times cited: 18
Funding	Funder: Sandia National Laboratories

Short KIM ID The unique KIM identifier code.	MO_036303866285_000
Extended KIM ID The long form of the KIM ID including a human readable prefix (100 characters max), two underscores, and the Short KIM ID. Extended KIM IDs can only contain alpha-numeric characters (letters and digits) and underscores and must begin with a letter.	EAM_Dynamo_ZhouFosterSills_2018_FeNiCr__MO_036303866285_000
DOI	10.25950/e28aaa47 https://doi.org/10.25950/e28aaa47 https://commons.datacite.org/doi.org/10.25950/e28aaa47
KIM Item Type Specifies whether this is a Portable Model (software implementation of an interatomic model); Portable Model with parameter file (parameter file to be read in by a Model Driver); Model Driver (software implementation of an interatomic model that reads in parameters).	Portable Model using Model Driver EAM_Dynamo__MD_120291908751_005
Driver	EAM_Dynamo__MD_120291908751_005
KIM API Version	2.2
Potential Type	eam
Programming Language(s) The programming languages used in the code and the percentage of the code written in each one. "N/A" means "not applicable" and refers to model parameterizations which only include parameter tables and have no programming language.	N/A

Verification Check Dashboard

(Click here to learn more about Verification Checks)

Grade	Name	Category	Brief Description	Full Results	Aux File(s)
P All elements claimed by model are supported in at least one of the tested configurations.	vc-species-supported-as-stated	mandatory	The model supports all species it claims to support; see full description.	Results	Files
P Periodic boundary conditions were correctly supported for all configurations that the model was able to compute.	vc-periodicity-support	mandatory	Periodic boundary conditions are handled correctly; see full description.	Results	Files
P Model energy has permutation symmetry for all configurations the model was able to compute.	vc-permutation-symmetry	mandatory	Total energy and forces are unchanged when swapping atoms of the same species; see full description.	Results	Files
F The model is C^0 continuous. This means that the model has continuous energy, but a discontinuous first derivative.	vc-dimer-continuity-c1	informational	The energy versus separation relation of a pair of atoms is C1 continuous (i.e. the function and its first derivative are continuous); see full description.	Results	Files
P Model energy and forces are invariant with respect to rigid-body motion (translation and rotation) for all configurations the model was able to compute.	vc-objectivity	informational	Total energy is unchanged and forces transform correctly under rigid-body translation and rotation; see full description.	Results	Files
P Model energy has inversion symmetry for all configurations the model was able to compute.	vc-inversion-symmetry	informational	Total energy is unchanged and forces change sign when inverting a configuration through the origin; see full description.	Results	Files
N/A Unable to perform verification check due to an error.	vc-memory-leak	informational	The model code does not have memory leaks (i.e. it releases all allocated memory at the end); see full description.	Results	Files
P All threads give identical results for tested case. Model appears to be thread-safe.	vc-thread-safe	mandatory	The model returns the same energy and forces when computed in serial and when using parallel threads for a set of configurations. Note that this is not a guarantee of thread safety; see full description.	Results	Files
P Unit conversion successful	vc-unit-conversion	mandatory	The model is able to correctly convert its energy and/or forces to different unit sets; see full description.	Results	Files

This bar chart plot shows the mono-atomic body-centered cubic (bcc) lattice constant predicted by the current model (shown in the unique color) compared with the predictions for all other models in the OpenKIM Repository that support the species. The vertical bars show the average and standard deviation (one sigma) bounds for all model predictions. Graphs are generated for each species supported by the model.

Species: Ni

Species: Fe

Species: Cr

Cohesive Energy Graph

This graph shows the cohesive energy versus volume-per-atom for the current mode for four mono-atomic cubic phases (body-centered cubic (bcc), face-centered cubic (fcc), simple cubic (sc), and diamond). The curve with the lowest minimum is the ground state of the crystal if stable. (The crystal structure is enforced in these calculations, so the phase may not be stable.) Graphs are generated for each species supported by the model.

Species: Fe

Species: Cr

Species: Ni

Diamond Lattice Constant

This bar chart plot shows the mono-atomic face-centered diamond lattice constant predicted by the current model (shown in the unique color) compared with the predictions for all other models in the OpenKIM Repository that support the species. The vertical bars show the average and standard deviation (one sigma) bounds for all model predictions. Graphs are generated for each species supported by the model.

Species: Fe

Species: Cr

Species: Ni

Dislocation Core Energies

This graph shows the dislocation core energy of a cubic crystal at zero temperature and pressure for a specific set of dislocation core cutoff radii. After obtaining the total energy of the system from conjugate gradient minimizations, non-singular, isotropic and anisotropic elasticity are applied to obtain the dislocation core energy for each of these supercells with different dipole distances. Graphs are generated for each species supported by the model.

(No matching species)

FCC Elastic Constants

This bar chart plot shows the mono-atomic face-centered cubic (fcc) elastic constants predicted by the current model (shown in blue) compared with the predictions for all other models in the OpenKIM Repository that support the species. The vertical bars show the average and standard deviation (one sigma) bounds for all model predictions. Graphs are generated for each species supported by the model.

Species: Ni

Species: Fe

Species: Cr

FCC Lattice Constant

This bar chart plot shows the mono-atomic face-centered cubic (fcc) lattice constant predicted by the current model (shown in red) compared with the predictions for all other models in the OpenKIM Repository that support the species. The vertical bars show the average and standard deviation (one sigma) bounds for all model predictions. Graphs are generated for each species supported by the model.

Species: Ni

Species: Cr

Species: Fe

FCC Stacking Fault Energies

This bar chart plot shows the intrinsic and extrinsic stacking fault energies as well as the unstable stacking and unstable twinning energies for face-centered cubic (fcc) predicted by the current model (shown in blue) compared with the predictions for all other models in the OpenKIM Repository that support the species. The vertical bars show the average and standard deviation (one sigma) bounds for all model predictions. Graphs are generated for each species supported by the model.

Species: Ni

FCC Surface Energies

This bar chart plot shows the mono-atomic face-centered cubic (fcc) relaxed surface energies predicted by the current model (shown in blue) compared with the predictions for all other models in the OpenKIM Repository that support the species. The vertical bars show the average and standard deviation (one sigma) bounds for all model predictions. Graphs are generated for each species supported by the model.

Species: Ni

SC Lattice Constant

This bar chart plot shows the mono-atomic simple cubic (sc) lattice constant predicted by the current model (shown in the unique color) compared with the predictions for all other models in the OpenKIM Repository that support the species. The vertical bars show the average and standard deviation (one sigma) bounds for all model predictions. Graphs are generated for each species supported by the model.

Species: Fe

Species: Cr

Species: Ni

Cubic Crystal Basic Properties Table

Species: Cr

Species: Fe

Species: Ni

Tests

CohesiveEnergyVsLatticeConstant__TD_554653289799_003

Cohesive energy versus lattice constant curve for monoatomic cubic lattices v003

Creators:
Contributor: karls
Publication Year: 2019
DOI: https://doi.org/10.25950/64cb38c5

This Test Driver uses LAMMPS to compute the cohesive energy of a given monoatomic cubic lattice (fcc, bcc, sc, or diamond) at a variety of lattice spacings. The lattice spacings range from a_min (=a_min_frac*a_0) to a_max (=a_max_frac*a_0) where a_0, a_min_frac, and a_max_frac are read from stdin (a_0 is typically approximately equal to the equilibrium lattice constant). The precise scaling and number of lattice spacings sampled between a_min and a_0 (a_0 and a_max) is specified by two additional parameters passed from stdin: N_lower and samplespacing_lower (N_upper and samplespacing_upper). Please see README.txt for further details.

Test	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Cohesive energy versus lattice constant curve for bcc Cr v004	view	4734
Cohesive energy versus lattice constant curve for bcc Fe v004	view	8908
Cohesive energy versus lattice constant curve for bcc Ni v004	view	9430
Cohesive energy versus lattice constant curve for diamond Cr v004	view	5440
Cohesive energy versus lattice constant curve for diamond Fe v004	view	6326
Cohesive energy versus lattice constant curve for diamond Ni v004	view	6515
Cohesive energy versus lattice constant curve for fcc Cr v004	view	4794
Cohesive energy versus lattice constant curve for fcc Fe v004	view	6226
Cohesive energy versus lattice constant curve for fcc Ni v004	view	6525
Cohesive energy versus lattice constant curve for sc Cr v004	view	5092
Cohesive energy versus lattice constant curve for sc Fe v004	view	17033
Cohesive energy versus lattice constant curve for sc Ni v004	view	7052

ElasticConstantsCrystal__TD_034002468289_000

Elastic constants for arbitrary crystals at zero temperature and pressure v000

Creators:
Contributor: ilia
Publication Year: 2024
DOI: https://doi.org/10.25950/888f9943

Computes the elastic constants for an arbitrary crystal. A robust computational protocol is used, attempting multiple methods and step sizes to achieve an acceptably low error in numerical differentiation and deviation from material symmetry. The crystal structure is specified using the AFLOW prototype designation as part of the Crystal Genome testing framework. In addition, the distance from the obtained elasticity tensor to the nearest isotropic tensor is computed.

Test	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Elastic constants for CrFe in AFLOW crystal prototype A2B_cF24_227_c_b at zero temperature and pressure v000	view	86644
Elastic constants for CrNi in AFLOW crystal prototype A2B_cF24_227_c_b at zero temperature and pressure v000	view	148051
Elastic constants for FeNi in AFLOW crystal prototype A2B_cF24_227_c_b at zero temperature and pressure v000	view	160714

ElasticConstantsCubic__TD_011862047401_006

Elastic constants for cubic crystals at zero temperature and pressure v006

Creators: Junhao Li and Ellad Tadmor
Contributor: tadmor
Publication Year: 2019
DOI: https://doi.org/10.25950/5853fb8f

Computes the cubic elastic constants for some common crystal types (fcc, bcc, sc, diamond) by calculating the hessian of the energy density with respect to strain. An estimate of the error associated with the numerical differentiation performed is reported.

Test	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Elastic constants for bcc Cr at zero temperature v006	view	37387
Elastic constants for bcc Fe at zero temperature v006	view	16858
Elastic constants for bcc Ni at zero temperature v006	view	8051
Elastic constants for fcc Cr at zero temperature v006	view	16063
Elastic constants for fcc Fe at zero temperature v006	view	16451
Elastic constants for fcc Ni at zero temperature v006	view	16222
Elastic constants for sc Cr at zero temperature v006	view	32688
Elastic constants for sc Fe at zero temperature v006	view	8088
Elastic constants for sc Ni at zero temperature v006	view	15118

EquilibriumCrystalStructure__TD_457028483760_002

Equilibrium structure and energy for a crystal structure at zero temperature and pressure v002

Creators:
Contributor: ilia
Publication Year: 2024
DOI: https://doi.org/10.25950/2f2c4ad3

Computes the equilibrium crystal structure and energy for an arbitrary crystal at zero temperature and applied stress by performing symmetry-constrained relaxation. The crystal structure is specified using the AFLOW prototype designation. Multiple sets of free parameters corresponding to the crystal prototype may be specified as initial guesses for structure optimization. No guarantee is made regarding the stability of computed equilibria, nor that any are the ground state.

Test	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Equilibrium crystal structure and energy for CrFe in AFLOW crystal prototype A2B_cF24_227_c_b v002	view	207981
Equilibrium crystal structure and energy for CrNi in AFLOW crystal prototype A2B_cF24_227_c_b v002	view	208650
Equilibrium crystal structure and energy for FeNi in AFLOW crystal prototype A2B_cF24_227_c_b v002	view	274163
Equilibrium crystal structure and energy for CrNi in AFLOW crystal prototype A3B_cF16_225_ac_b v002	view	114112
Equilibrium crystal structure and energy for FeNi in AFLOW crystal prototype A3B_cF16_225_ac_b v002	view	105054
Equilibrium crystal structure and energy for CrFe in AFLOW crystal prototype A3B_cP4_221_c_a v002	view	80932
Equilibrium crystal structure and energy for CrNi in AFLOW crystal prototype A3B_cP4_221_c_a v002	view	71044
Equilibrium crystal structure and energy for FeNi in AFLOW crystal prototype A3B_cP4_221_c_a v002	view	69719
Equilibrium crystal structure and energy for CrFe in AFLOW crystal prototype A3B_tI8_139_ad_b v002	view	56628
Equilibrium crystal structure and energy for FeNi in AFLOW crystal prototype A3B_tI8_139_ad_b v002	view	78627
Equilibrium crystal structure and energy for Cr in AFLOW crystal prototype A_cF4_225_a v002	view	94013
Equilibrium crystal structure and energy for Fe in AFLOW crystal prototype A_cF4_225_a v002	view	85672
Equilibrium crystal structure and energy for Ni in AFLOW crystal prototype A_cF4_225_a v002	view	52922
Equilibrium crystal structure and energy for Cr in AFLOW crystal prototype A_cI2_229_a v002	view	79731
Equilibrium crystal structure and energy for Fe in AFLOW crystal prototype A_cI2_229_a v002	view	85326
Equilibrium crystal structure and energy for Ni in AFLOW crystal prototype A_cI2_229_a v002	view	64406
Equilibrium crystal structure and energy for Cr in AFLOW crystal prototype A_cP8_223_ac v002	view	103414
Equilibrium crystal structure and energy for Cr in AFLOW crystal prototype A_hP2_194_c v002	view	71780
Equilibrium crystal structure and energy for Fe in AFLOW crystal prototype A_hP2_194_c v002	view	46664
Equilibrium crystal structure and energy for Ni in AFLOW crystal prototype A_hP2_194_c v002	view	44294
Equilibrium crystal structure and energy for Cr in AFLOW crystal prototype A_tP28_136_f2ij v002	view	234629
Equilibrium crystal structure and energy for Fe in AFLOW crystal prototype A_tP28_136_f2ij v002	view	65621
Equilibrium crystal structure and energy for CrFe in AFLOW crystal prototype AB2_cF24_227_a_d v002	view	350654
Equilibrium crystal structure and energy for CrNi in AFLOW crystal prototype AB2_cF24_227_a_d v002	view	229308
Equilibrium crystal structure and energy for FeNi in AFLOW crystal prototype AB2_cF24_227_a_d v002	view	294850
Equilibrium crystal structure and energy for CrFe in AFLOW crystal prototype AB3_cF16_225_a_bc v002	view	80750
Equilibrium crystal structure and energy for CrNi in AFLOW crystal prototype AB3_cF16_225_a_bc v002	view	77226
Equilibrium crystal structure and energy for FeNi in AFLOW crystal prototype AB3_cF16_225_a_bc v002	view	81358
Equilibrium crystal structure and energy for CrFe in AFLOW crystal prototype AB3_cP4_221_a_c v002	view	57175
Equilibrium crystal structure and energy for FeNi in AFLOW crystal prototype AB3_cP4_221_a_c v002	view	53712
Equilibrium crystal structure and energy for FeNi in AFLOW crystal prototype AB3_tI8_139_a_bd v002	view	85400
Equilibrium crystal structure and energy for FeNi in AFLOW crystal prototype AB_tP2_123_a_d v002	view	67731

GrainBoundaryCubicCrystalSymmetricTiltRelaxedEnergyVsAngle__TD_410381120771_003

Relaxed energy as a function of tilt angle for a symmetric tilt grain boundary within a cubic crystal v003

Creators:
Contributor: brunnels
Publication Year: 2022
DOI: https://doi.org/10.25950/2c59c9d6

Computes grain boundary energy for a range of tilt angles given a crystal structure, tilt axis, and material.

Test	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Relaxed energy as a function of tilt angle for a 100 symmetric tilt grain boundary in bcc Fe v001	view	6554878
Relaxed energy as a function of tilt angle for a 110 symmetric tilt grain boundary in bcc Fe v001	view	11304962
Relaxed energy as a function of tilt angle for a 111 symmetric tilt grain boundary in bcc Fe v001	view	5983096
Relaxed energy as a function of tilt angle for a 112 symmetric tilt grain boundary in bcc Fe v001	view	21106347
Relaxed energy as a function of tilt angle for a 100 symmetric tilt grain boundary in fcc Fe v001	view	5781093
Relaxed energy as a function of tilt angle for a 100 symmetric tilt grain boundary in fcc Ni v001	view	9638539
Relaxed energy as a function of tilt angle for a 110 symmetric tilt grain boundary in fcc Fe v001	view	33463845
Relaxed energy as a function of tilt angle for a 110 symmetric tilt grain boundary in fcc Ni v001	view	31189284
Relaxed energy as a function of tilt angle for a 111 symmetric tilt grain boundary in fcc Fe v001	view	10932872
Relaxed energy as a function of tilt angle for a 111 symmetric tilt grain boundary in fcc Ni v001	view	19230451
Relaxed energy as a function of tilt angle for a 112 symmetric tilt grain boundary in fcc Fe v001	view	61827837
Relaxed energy as a function of tilt angle for a 112 symmetric tilt grain boundary in fcc Ni v001	view	58267653

LatticeConstantCubicEnergy__TD_475411767977_007

Equilibrium lattice constant and cohesive energy of a cubic lattice at zero temperature and pressure v007

Creators: Daniel S. Karls and Junhao Li
Contributor: karls
Publication Year: 2019
DOI: https://doi.org/10.25950/2765e3bf

Equilibrium lattice constant and cohesive energy of a cubic lattice at zero temperature and pressure.

Test	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Equilibrium zero-temperature lattice constant for bcc Cr v007	view	10781
Equilibrium zero-temperature lattice constant for bcc Fe v007	view	11040
Equilibrium zero-temperature lattice constant for bcc Ni v007	view	10891
Equilibrium zero-temperature lattice constant for diamond Cr v007	view	11219
Equilibrium zero-temperature lattice constant for diamond Fe v007	view	11289
Equilibrium zero-temperature lattice constant for diamond Ni v007	view	7454
Equilibrium zero-temperature lattice constant for fcc Cr v007	view	5181
Equilibrium zero-temperature lattice constant for fcc Fe v007	view	11010
Equilibrium zero-temperature lattice constant for fcc Ni v007	view	7976
Equilibrium zero-temperature lattice constant for sc Cr v007	view	10861
Equilibrium zero-temperature lattice constant for sc Fe v007	view	10682
Equilibrium zero-temperature lattice constant for sc Ni v007	view	4659

LatticeConstantHexagonalEnergy__TD_942334626465_005

Equilibrium lattice constants for hexagonal bulk structures at zero temperature and pressure v005

Creators: Daniel S. Karls and Junhao Li
Contributor: karls
Publication Year: 2019
DOI: https://doi.org/10.25950/c339ca32

Calculates lattice constant of hexagonal bulk structures at zero temperature and pressure by using simplex minimization to minimize the potential energy.

Test	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Equilibrium lattice constants for hcp Cr v005	view	106896
Equilibrium lattice constants for hcp Fe v005	view	96274
Equilibrium lattice constants for hcp Ni v005	view	143939

LinearThermalExpansionCoeffCubic__TD_522633393614_002

Linear thermal expansion coefficient of cubic crystal structures v002

Creators:
Contributor: mjwen
Publication Year: 2024
DOI: https://doi.org/10.25950/9d9822ec

This Test Driver uses LAMMPS to compute the linear thermal expansion coefficient at a finite temperature under a given pressure for a cubic lattice (fcc, bcc, sc, diamond) of a single given species.

Test	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Linear thermal expansion coefficient of bcc Cr at 293.15 K under a pressure of 0 MPa v002	view	383517
Linear thermal expansion coefficient of bcc Fe at 293.15 K under a pressure of 0 MPa v002	view	454849
Linear thermal expansion coefficient of fcc Ni at 293.15 K under a pressure of 0 MPa v002	view	786417

PhononDispersionCurve__TD_530195868545_004

Phonon dispersion relations for an fcc lattice v004

Creators: Matt Bierbaum
Contributor: mattbierbaum
Publication Year: 2019
DOI: https://doi.org/10.25950/64f4999b

Calculates the phonon dispersion relations for fcc lattices and records the results as curves.

Test	Test Results	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Phonon dispersion relations for fcc Ni v004		view	100497

StackingFaultFccCrystal__TD_228501831190_002

Stacking and twinning fault energies of an fcc lattice at zero temperature and pressure v002

Creators:
Contributor: SubrahmanyamPattamatta
Publication Year: 2019
DOI: https://doi.org/10.25950/b4cfaf9a

Intrinsic and extrinsic stacking fault energies, unstable stacking fault energy, unstable twinning energy, stacking fault energy as a function of fractional displacement, and gamma surface for a monoatomic FCC lattice at zero temperature and pressure.

Test	Test Results	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Stacking and twinning fault energies for fcc Ni v002		view	17640092

SurfaceEnergyCubicCrystalBrokenBondFit__TD_955413365818_004

High-symmetry surface energies in cubic lattices and broken bond model v004

Creators: Matt Bierbaum
Contributor: mattbierbaum
Publication Year: 2019
DOI: https://doi.org/10.25950/6c43a4e6

Calculates the surface energy of several high symmetry surfaces and produces a broken-bond model fit. In latex form, the fit equations are given by:

E_{FCC} (\vec{n}) = p_1 (4 \left( |x+y| + |x-y| + |x+z| + |x-z| + |z+y| +|z-y|\right)) + p_2 (8 \left( |x| + |y| + |z|\right)) + p_3 (2 ( |x+ 2y + z| + |x+2y-z| + |x-2y + z| + |x-2y-z| + |2x+y+z| + |2x+y-z| +|2x-y+z| +|2x-y-z| +|x+y+2z| +|x+y-2z| +|x-y+2z| +|x-y-2z| ) + c

E_{BCC} (\vec{n}) = p_1 (6 \left( | x+y+z| + |x+y-z| + |-x+y-z| + |x-y+z| \right)) + p_2 (8 \left( |x| + |y| + |z|\right)) + p_3 (4 \left( |x+y| + |x-y| + |x+z| + |x-z| + |z+y| +|z-y|\right)) +c.

In Python, these two fits take the following form:

def BrokenBondFCC(params, index):

import numpy
x, y, z = index
x = x / numpy.sqrt(x**2.+y**2.+z**2.)
y = y / numpy.sqrt(x**2.+y**2.+z**2.)
z = z / numpy.sqrt(x**2.+y**2.+z**2.)

return params[0]*4* (abs(x+y) + abs(x-y) + abs(x+z) + abs(x-z) + abs(z+y) + abs(z-y)) + params[1]*8*(abs(x) + abs(y) + abs(z)) + params[2]*(abs(x+2*y+z) + abs(x+2*y-z) +abs(x-2*y+z) +abs(x-2*y-z) + abs(2*x+y+z) +abs(2*x+y-z) +abs(2*x-y+z) +abs(2*x-y-z) + abs(x+y+2*z) +abs(x+y-2*z) +abs(x-y+2*z) +abs(x-y-2*z))+params[3]

def BrokenBondBCC(params, x, y, z):

import numpy
x, y, z = index
x = x / numpy.sqrt(x**2.+y**2.+z**2.)
y = y / numpy.sqrt(x**2.+y**2.+z**2.)
z = z / numpy.sqrt(x**2.+y**2.+z**2.)

return params[0]*6*(abs(x+y+z) + abs(x-y-z) + abs(x-y+z) + abs(x+y-z)) + params[1]*8*(abs(x) + abs(y) + abs(z)) + params[2]*4* (abs(x+y) + abs(x-y) + abs(x+z) + abs(x-z) + abs(z+y) + abs(z-y)) + params[3]

Test	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Broken-bond fit of high-symmetry surface energies in bcc Cr v004	view	77410
Broken-bond fit of high-symmetry surface energies in bcc Fe v004	view	78066
Broken-bond fit of high-symmetry surface energies in fcc Ni v004	view	97162

VacancyFormationEnergyRelaxationVolume__TD_647413317626_001

Monovacancy formation energy and relaxation volume for cubic and hcp monoatomic crystals v001

Creators:
Contributor: efuem
Publication Year: 2023
DOI: https://doi.org/10.25950/fca89cea

Computes the monovacancy formation energy and relaxation volume for cubic and hcp monoatomic crystals.

Test	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Monovacancy formation energy and relaxation volume for bcc Cr	view	390041
Monovacancy formation energy and relaxation volume for bcc Fe	view	471245
Monovacancy formation energy and relaxation volume for fcc Ni	view	356691

VacancyFormationMigration__TD_554849987965_001

Vacancy formation and migration energies for cubic and hcp monoatomic crystals v001

Creators:
Contributor: efuem
Publication Year: 2023
DOI: https://doi.org/10.25950/c27ba3cd

Computes the monovacancy formation and migration energies for cubic and hcp monoatomic crystals.

Test	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Vacancy formation and migration energy for bcc Cr	view	3609539
Vacancy formation and migration energy for bcc Fe	view	5675183
Vacancy formation and migration energy for fcc Ni	view	943594

Errors

ElasticConstantsCubic__TD_011862047401_006

Test	Error Categories	Link to Error page
Elastic constants for diamond Cr at zero temperature v001	other	view
Elastic constants for diamond Fe at zero temperature v001	other	view
Elastic constants for diamond Ni at zero temperature v001	other	view

ElasticConstantsHexagonal__TD_612503193866_004

Test	Error Categories	Link to Error page
Elastic constants for hcp Cr at zero temperature v004	other	view
Elastic constants for hcp Fe at zero temperature v004	other	view
Elastic constants for hcp Ni at zero temperature v004	other	view

EquilibriumCrystalStructure__TD_457028483760_000

Test	Error Categories	Link to Error page
Equilibrium crystal structure and energy for CrFe in AFLOW crystal prototype AB3_cP4_221_a_c v000	other	view

EquilibriumCrystalStructure__TD_457028483760_002

Test	Error Categories	Link to Error page
Equilibrium crystal structure and energy for Fe in AFLOW crystal prototype A_tP1_123_a v002	other	view

PhononDispersionCurve__TD_530195868545_004

Test	Error Categories	Link to Error page
Phonon dispersion relations for fcc Ni v004	other	view

SurfaceEnergyCubicCrystalBrokenBondFit__TD_955413365818_004

Test	Error Categories	Link to Error page
Broken-bond fit of high-symmetry surface energies in bcc Cr v004	other	view
Broken-bond fit of high-symmetry surface energies in bcc Fe v004	other	view
Broken-bond fit of high-symmetry surface energies in fcc Ni v004	other	view

No Driver

Verification Check	Error Categories	Link to Error page
ForcesNumerDeriv__VC_710586816390_003	other	view
MemoryLeak__VC_561022993723_004	other	view

Files

CMakeLists.txt
FeNiCr.t
LICENSE
kimprovenance.edn
kimspec.edn

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EAM_Dynamo_ZhouFosterSills_2018_FeNiCr__MO_036303866285_000.txz	Tar+XZ	Linux and OS X archive
EAM_Dynamo_ZhouFosterSills_2018_FeNiCr__MO_036303866285_000.zip	Zip	Windows archive

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This Model requires a Model Driver. Archives for the Model Driver EAM_Dynamo__MD_120291908751_005 appear below.

EAM_Dynamo__MD_120291908751_005.txz	Tar+XZ	Linux and OS X archive
EAM_Dynamo__MD_120291908751_005.zip	Zip	Windows archive

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EAM_Dynamo_ZhouFosterSills_2018_FeNiCr__MO_036303866285_000

Verification Check Dashboard

Visualizers (in-page)

BCC Lattice Constant

Cohesive Energy Graph

Diamond Lattice Constant

Dislocation Core Energies

FCC Elastic Constants

FCC Lattice Constant

FCC Stacking Fault Energies

FCC Surface Energies

SC Lattice Constant

Cubic Crystal Basic Properties Table

Tests

Errors

Files

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Wiki