EAM potential for Cu–Ni developed by Fischer et al. (2019) v000

Felix Fischer; Guido Schmitz; Sebastian M. Eich

doi:10.25950/ca482d5d

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EAM_Dynamo_FischerSchmitzEich_2019_CuNi__MO_266134052596_000

Interatomic potential for Copper (Cu), Nickel (Ni).
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Title A single sentence description.	EAM potential for Cu–Ni developed by Fischer et al. (2019) 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.	In this atomistic study on the copper–nickel system, a new embedded-atom alloy potential between copper and nickel is fitted to experimental data on the mixing enthalpy, taking available potentials for the pure components from literature. The resulting phase boundaries of the new potential are in very good agreement with a recent CALPHAD prediction. Using this new potential, a high angle symmetrical tilt Σ5 and a coherent Σ3 twin grain boundary (GB) are chosen for a systematic investigation of equilibrium GB segregation in the semi-grandcanonical ensemble at temperatures from 400 K to 800 K. Applying thermodynamically accurate integration techniques, the GB formation energies are calculated exactly and as an absolute value for every temperature and composition, which also enables the evaluation of GB excess entropies. The thorough thermodynamic model of GBs developed by Frolov and Mishin is excellently confirmed by the simulations quantitatively, if the impact of both segregation and GB tension on the change in GB formation energy is accounted for. In the case of the Σ3 coherent GB, it turns out that the change in GB formation energy at low temperatures is for the most part attributed to the GB tension, while segregation only has a small influence. This demonstrated effect of GB tensions should also be taken into account in the interpretation of experiments.
Species The supported atomic species.	Cu, 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/2019--Fischer-F-Schmitz-G-Eich-S-M--Cu-Ni/2019--Fischer-F--Cu-Ni--LAMMPS--ipr3.html

Contributor	Sebastian M. Eich
Maintainer	Sebastian M. Eich
Developer	Felix Fischer Guido Schmitz Sebastian M. Eich
Published on KIM	2021
How to Cite	This Model originally published in [1] is archived in OpenKIM [2-5]. [1] Fischer F, Schmitz G, Eich SM. A systematic study of grain boundary segregation and grain boundary formation energy using a new copper–nickel embedded-atom potential. Acta Mater [Internet]. 2019;176:220–31. Available from: https://www.sciencedirect.com/science/article/pii/S1359645419303945 doi:10.1016/j.actamat.2019.06.027 — (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] Fischer F, Schmitz G, Eich SM. EAM potential for Cu–Ni developed by Fischer et al. (2019) v000. OpenKIM; 2021. doi:10.25950/ca482d5d [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. 30 Citations (28 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 . H. Jo et al., “Direct strain correlations at the single-atom level in three-dimensional core-shell interface structures,” Nature Communications. 2022. link Times cited: 8 R. Garza et al., “Atomistic Mechanisms of Binary Alloy Surface Segregation from Nanoseconds to Seconds Using Accelerated Dynamics.,” Journal of chemical theory and computation. 2021. link Times cited: 2 Abstract: Although the equilibrium composition of many alloy surfaces … read more Abstract: Although the equilibrium composition of many alloy surfaces is well understood, the rate of transient surface segregation during annealing is not known, despite its crucial effect on alloy corrosion and catalytic reactions occurring on overlapping timescales. In this work, CuNi bimetallic alloys representing (100) surface facets are annealed in vacuum using atomistic simulations to observe the effect of vacancy diffusion on surface separation. We employ multi-timescale methods to sample the early transient, intermediate, and equilibrium states of slab surfaces during the separation process, including standard MD as well as three methods to perform atomistic, long-time dynamics: parallel trajectory splicing (ParSplice), adaptive kinetic Monte Carlo (AKMC), and kinetic Monte Carlo (KMC). From nanosecond (ns) to second timescales, our multiscale computational methodology can observe rare stochastic events not typically seen with standard MD, closing the gap between computational and experimental timescales for surface segregation. Rapid diffusion of a vacancy to the slab is resolved by all four methods in tens of nanoseconds. Stochastic re-entry of vacancies into the subsurface, however, is only seen on the microsecond timescale in the two KMC methods. Kinetic vacancy trapping on the surface and its effect on the segregation rate are discussed. The equilibrium composition profile of CuNi after segregation during annealing is estimated to occur on a timescale of seconds as determined by KMC, a result directly comparable to nanoscale experiments. read less A. Verma, O. K. Johnson, G. B. Thompson, S. Ogata, and E. R. Homer, “Solute influence in transitions from non-Arrhenius to stick-slip Arrhenius grain boundary migration,” Acta Materialia. 2023. link Times cited: 0 M. Chen, “The Optimum Grain Size of Nanocrystalline CuNi Alloy with Grain Boundary Segregation Structure,” Crystal Growth & Design. 2023. link Times cited: 0 Y. Gao et al., “Investigation of deformation mechanism of SiC–CuNi composite thin film material nanochannels by molecular dynamics simulation,” Results in Physics. 2023. link Times cited: 0 N. Chen, A. Devaraj, S. Mathaudhu, and S. Hu, “Atomic mixing mechanisms in nanocrystalline Cu/Ni composites under continuous shear deformation and thermal annealing,” Journal of Materials Research and Technology. 2023. link Times cited: 0 X. Lu et al., “Effect of twin boundary spacing on the mechanical properties of nano-columnar crystalline Cu-Ni alloy,” Molecular Simulation. 2023. link Times cited: 0 Abstract: ABSTRACT Nanotwinned exist in crystals as coherent interface… read more Abstract: ABSTRACT Nanotwinned exist in crystals as coherent interfaces with low interfacial energy, which can not only improve the strength of metal materials, but also increase the ductility. In this manuscript, we have performed molecular dynamics simulations of the mechanical properties of a nano-columnar crystalline Cu-Ni alloy with different twin boundary spacing. It is found that the model without twin has a stacking fault tetrahedron composed of stair-rod dislocations, which results in a small change in dislocation density at the later stage of deformation, and the average stress after yielding is lower than that of the model with twin. During the deformation process, with the increase of Other atoms, the dislocation slip barrier is enhanced, the tensile strength is increased, and the yield phenomenon is delayed, which is more obvious with the decrease of twin boundary spacing. The dislocation density decreases with the decrease of the spacing of the twin boundary, and the dislocation segments become shorter. When the twin boundary spacing is 0.625 nm, the tensile strength is increased by about 71% compared with the model without twin structure. read less T. L. Dora, S. K. Singh, R. R. Mishra, and A. Verma, “Role of Crystal Orientation, Temperature, and Strain Rate on the Mechanical Characterization of Nickel: An Atomistic-scale investigation,” Journal of Micromanufacturing. 2023. link Times cited: 1 Abstract: In this article, the influence of crystallographic orientati… read more Abstract: In this article, the influence of crystallographic orientation on the mechanical properties of pristine nickel (Ni) during uniaxial tensile deformation was explored by utilizing molecular dynamics simulations. To study the influence of [0 01] and [11 8 5] crystal orientations on the mechanical properties and microstructural evolution of pristine Ni, simulations were performed at different temperatures ranging from 100 K to 900 K and at strain rates ranging from 10 7 to 10 10 s–1. The results revealed that Ni with [11 8 5] orientation showed a higher elastic modulus than Ni with [0 0 1] orientation, whereas the yield strength of [0 0 1] orientation was higher than [11 8 5] orientation for a combination of temperatures and strain rates. Also, in comparison to [11 8 5] crystal orientation, the system with [0 0 1] orientation showed a high amount of dislocation density at the yield strain point for lower strain rates. At higher strain rates, the face-centered cubic to body-centered cubic transition was more prominent in the Ni system with [0 0 1] orientation, and it tends to decrease with the increase in temperature. Our present work may help materials scientists design materials with different crystal orientations that can perform according to the applied strain rates and temperatures. It is also proposed that tailoring of mechanical properties is achievable by exposing Ni with different crystal orientations to various environmental conditions (cryogenic, ambient, and elevated temperatures with different applied strain rates). read less A. Verma, O. K. Johnson, G. Thompson, I. Chesser, S. Ogata, and E. Homer, “Insights into factors that affect non-Arrhenius migration of a simulated incoherent Σ3 grain boundary,” Acta Materialia. 2023. link Times cited: 2 H. Peng et al., “Correlation between stabilizing and strengthening effects due to grain boundary segregation in iron-based alloys: theoretical models and first-principles calculations,” Acta Materialia. 2023. link Times cited: 4 S. Yazdani and V. Vitry, “Using Molecular Dynamic Simulation to Understand the Deformation Mechanism in Cu, Ni, and Equimolar Cu-Ni Polycrystalline Alloys,” Alloys. 2023. link Times cited: 3 Abstract: The grain boundaries and dislocations play an important role… read more Abstract: The grain boundaries and dislocations play an important role in understanding the deformation behavior in polycrystalline materials. In this paper, the deformation mechanism of Cu, Ni, and equimolar Cu-Ni alloy was investigated using molecular dynamic simulation. The interaction between dislocations and grain boundary motion during the deformation was monitored using the dislocation extraction algorithm. Moreover, the effect of stacking fault formation and atomic band structure on the deformation behavior was discussed. Results indicate that dislocations nucleate around the grain boundary in copper, the deformation in nickel changes from planar slip bands to wavy bands, and high density of dislocation accumulation as well as numerous kink and jog formations were observed for the equimolar Cu-Ni alloy. The highest density of the Shockley dislocation and stacking faults was formed in the equimolar Cu-Ni alloy which results in the appearance of a huge gliding stage in the stress–strain curve. The grain boundaries act as a sinking source for vacancy annihilation in Ni and Cu; however, this effect was not observed in an equimolar Cu-Ni alloy. Finally, radial distribution function was used to evaluate atom segregation in grain boundaries. read less C. Hu, S. Berbenni, D. Medlin, and R. Dingreville, “Discontinuous segregation patterning across disconnections,” Acta Materialia. 2023. link Times cited: 3 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 J. Liu, H. Li, B. Liu, L. Wang, J. Zhou, and F. Zhang, “The hardening and softening mechanism governed by GB stability in nanograined metals: A molecular dynamics study,” Materials Today Communications. 2022. link Times cited: 0 L. Granger, M.-J. Chen, D. Brenner, and M. Zikry, “The Challenges of Modeling Defect Behavior and Plasticity across Spatial and Temporal Scales: A Case Study of Metal Bilayer Impact,” Metals. 2022. link Times cited: 1 Abstract: Atomistic molecular dynamics (MD) and a microstructural disl… read more Abstract: Atomistic molecular dynamics (MD) and a microstructural dislocation density-based crystalline plasticity (DCP) framework were used together across time scales varying from picoseconds to nanoseconds and length scales spanning from angstroms to micrometers to model a buried copper–nickel interface subjected to high strain rates. The nucleation and evolution of defects, such as dislocations and stacking faults, as well as large inelastic strain accumulations and wave-induced stress reflections were physically represented in both approaches. Both methods showed similar qualitative behavior, such as defects originating along the impactor edges, a dominance of Shockley partial dislocations, and non-continuous dislocation distributions across the buried interface. The favorable comparison between methods justifies assumptions used in both, to model phenomena, such as the nucleation and interactions of single defects and partials with reflected tensile waves, based on MD predictions, which are consistent with the evolution of perfect and partial dislocation densities as predicted by DCP. This substantiates how the nanoscale as modeled by MD is representative of microstructural behavior as modeled by DCP. read less J. Zhu, S. Huang, Z. Xie, H. Guo, and H. Yang, “Thermal Conductance of Copper–Graphene Interface: A Molecular Simulation,” Materials. 2022. link Times cited: 3 Abstract: Copper is often used as a heat-dissipating material due to i… read more Abstract: Copper is often used as a heat-dissipating material due to its high thermal conductivity. In order to improve its heat dissipation performance, one of the feasible methods is to compound copper with appropriate reinforcing phases. With excellent thermal properties, graphene has become an ideal reinforcing phase and displays great application prospects in metal matrix composites. However, systematic theoretical research is lacking on the thermal conductivity of the copper–graphene interface and associated affecting factors. Molecular dynamics simulation was used to simulate the interfacial thermal conductivity of copper/graphene composites, and the effects of graphene layer number, atomic structure, matrix length, and graphene vacancy rate on thermal boundary conductance (TBC) were investigated. The results show that TBC decreases with an increase in graphene layers and converges when the number of graphene layers is above five. The atomic structure of the copper matrix affects the TBC, which achieves the highest value with the (011) plane at the interface. The length of the copper matrix has little effect on the TBC. As the vacancy rate is between 0 and 4%, TBC increases with the vacancy rate. Our results present insights for future thermal management optimization based on copper matrix composites. read less Z. Sharipov et al., “Simulation of the Interaction Processes between Copper Nanoclusters and Metal Targets with Pore-Type Defects,” Journal of Surface Investigation: X-ray, Synchrotron and Neutron Techniques. 2022. link Times cited: 0 A.-S. Tran, “Influences of grain size and twin boundary on the tensile properties of nanocrystalline face-centered cubic Cu50Ni50 alloy,” Molecular Simulation. 2022. link Times cited: 1 Abstract: ABSTRACT The effects of grain size (GS) and distance betwee… read more Abstract: ABSTRACT The effects of grain size (GS) and distance between twin boundaries (d) on the tensile properties of nanocrystalline (NC) face-centered cubic Cu50Ni50 alloy are studied using the molecular dynamics simulations. The common neighbour analysis, dislocation extraction algorithm, shear strain, and von Mises stress configurations together with the total dislocation length, stress–strain relation, and tensile strength diagrams are presented to investigate the mechanical characteristics of the NC Cu50Ni50 specimens. The results exhibits that the twin boundaries significantly restrict the formation and propagation of the stacking faults. The integral deformation occurs under a combination of displacement, breakage, and self-destruction of the twin boundary along with the contraction and expansion of the grains. The tensile strength increases as the GS and the d increase, which agrees with the inverse Hall-Petch relation. The tensile strength of the conventional NC is higher than that of the twinned NC for each pair of specimens with the same GS. read less J. Li, J. Li, Q. Zhao, and R. Xia, “Molecular dynamics simulations on the mechanical properties of gyroidal bicontinuous Cu/Ni nanocomposites,” Journal of Materials Research and Technology. 2022. link Times cited: 5 X. Liu et al., “Coalescence kinetics and microstructure evolution of Cu nanoparticles sintering on substrates: A molecular dynamics study,” Journal of Materials Research and Technology. 2022. link Times cited: 5 U. Sarder, T. Paul, I. Belova, and G. Murch, “The Diffusion Isotope Effect and Diffusion Mechanism in Liquid Cu-Ag and Cu-Ni Alloys,” Defect and Diffusion Forum. 2021. link Times cited: 1 Abstract: In this paper, the diffusion isotope effect and diffusion me… read more Abstract: In this paper, the diffusion isotope effect and diffusion mechanism are investigated by means of molecular dynamics simulations in two liquid alloys, Ni-Ag and Ni-Cu. The values for the diffusion isotope effect parameter allow for the estimate of the number of atoms which are moving cooperatively in a basic diffusion event as experienced by a given atomic species. It is shown that the composition dependence of ND is typically very small. However, the temperature dependence of this parameter is much more pronounced. In addition, it is shown that, on average, in these alloys and temperatures considered, ND is limited to the range: 5read less H. Wang, C. Wang, L. Zhang, G. Chen, Q. Zhu, and P. Zhang, “Effect of Strain Rate on the Mechanical Properties of Cu/Ni Clad Foils,” Materials. 2021. link Times cited: 4 Abstract: The performance of clad foils in microforming deserves to be… read more Abstract: The performance of clad foils in microforming deserves to be studied extensively, where the strain rate sensitivity of the clad foil concerning the forming performance is a crucial factor. In this paper, the strain rate sensitivity of the mechanical properties of coarse-grained (CG) Cu/Ni clad foils in the quasi-static strain rate range (ε˙=10−4 s−1~10−1 s−1) is explored by uniaxial tensile tests under different strain rates. The results show that the strength and ductility increase with strain rate, and the strain rate sensitivity m value is in the range of 0.012~0.015, which is three times the value of m for CG pure Cu. The fracture morphology shows that slip bands with different directions are entangled in localized areas near the interface layer. Molecular dynamics simulations demonstrate the formation of many edged dislocations at the Cu/Ni clad foils interface due to a mismatch interface. The improved ductility and strain rate sensitivity is attributed to the interaction and plugging of the edged dislocations with high density in the interface layer. Additionally, the influence of size effect on mechanical properties is consistently present in the quasi-static strain rate range. This paper helps to understand the strain rate sensitivity of CG clad foils and to develop clad foils in microforming processes. read less D. Lu et al., “Effects of microstructure on tensile properties of AA2050-T84 Al-Li alloy,” Transactions of Nonferrous Metals Society of China. 2021. link Times cited: 10 P. Lejček, S. Hofmann, M. Všianská, and M. Šob, “Entropy matters in grain boundary segregation,” Acta Materialia. 2021. link Times cited: 19 F. Fischer and S. Eich, “Analytic description of grain boundary segregation, tension, and formation energy in the copper–nickel system,” Acta Materialia. 2020. link Times cited: 2 T. Krauss and S. Eich, “Development of a segregation model beyond McLean based on atomistic simulations,” Acta Materialia. 2020. link Times cited: 6 A. Nikonov, A. Dmitriev, and A. Smolin, “Selection of the potential for MD-modeling of phase martensitic transformations in Al-Cu-Ni alloy,” PROCEEDINGS OF THE INTERNATIONAL CONFERENCE “PHYSICAL MESOMECHANICS. MATERIALS WITH MULTILEVEL HIERARCHICAL STRUCTURE AND INTELLIGENT MANUFACTURING TECHNOLOGY.” 2022. link Times cited: 1 F. Fischer and S. Eich, “Analytic Description of Grain Boundary Segregation, Tension, and Formation Energy in the Copper-Nickel System,” Chemical Engineering (Engineering) eJournal. 2020. link Times cited: 0 Abstract: In this theoretical study, a recently proposed segregation m… read more Abstract: In this theoretical study, a recently proposed segregation model is applied to segregation data of an exemplary Σ5 grain boundary (GB), which is investigated using a copper-nickel embedded-atom method potential. Segregation in the semi-grandcanonical ensemble is systematically studied by varying the chemical potential in order to explore the full composition range for temperatures from 500 K to 1000 K. As a major thermodynamic feature, the mentioned segregation model avoids the usage of interface compositions, for which an arbitrary volume must be deﬁned, but rather models the thermodynamically unambiguous solute excess. It was shown that the solute excess and the interface formation energy could be described very accurately over a wide range of temperatures and over the entire composition range based on a composition-dependent, but temperature-independent effective energy of segregation. However,the model was initially derived for systems without lattice mismatch so that interface tensions were neglected. Since the copper-nickel system exhibits a moderate lattice mismatch of roughly 2.7 %, the copper-nickel system is chosen in this study to further extend the segregation model by a linear-elastic theory to also account for the interface tensions. Using this extended model, it will be shown that the solute excess, GB tensions, and GB formation energies can be derived from the effective energy of segregation for all temperatures and over the whole composition range. As in principle only one single segregation isotherm is sufﬁcient to determine the effective energy of segregation and derived thermodynamic interface quantities, the application of this model is especially interesting for experimental evaluations. read less T. L. Dora, S. K. Singh, R. R. Mishra, R. Das, J. Gupta, and A. Verma, “Unravelling the atomistic-scale insights into tensile response of equiatomic cupronickel alloy with pre-existing faceted grain boundary interface,” Results in Surfaces and Interfaces. 2023. link Times cited: 0 R. Garza et al., “Multiscale vacancy and dislocation-mediated surface segregation in CuNi alloy up to microsecond timescales with accelerated dynamics,” Microscopy and Microanalysis. 2021. link Times cited: 0 Abstract: Phase separation of CuNi in vacuum is well-documented in bot… read more Abstract: Phase separation of CuNi in vacuum is well-documented in both theory and experiment: annealing enriches the exposed surfaces with Cu and the subsurface layers with Ni [1, 2]. Even before exposure to air or corrosive environments, the bimetal restructures, affecting outcomes in both oxidation and reduction experiments. Transformations on the atomic and nanoscale could influence the study of Cu-Ni catalysts e.g. for the reduction of CO 2 , as well as in corrosion-resistant, marine alloys, making it difficult to replicate behavior between samples observed using microscopy techniques. Fig. 1 is an image taken during HRTEM observation and in situ oxidation of CuNi(100): the metals are impossible to distinguish within the alloy, since they display low lattice mismatch, and their oxides also exhibit little difference in d-spacing. Further, the atomic mechanisms, dynamics, and rate of this restructuring are difficult to observe, since the two metallic nuclei are difficult to distinguish even with Z-contrast, and elemental mapping is only available over long times (minutes to hours). The migration and coalescence of individual vacancies is also poorly resolved in experiment, yet the flow of point defects towards an exposed surface is directly related to the well-known counterflow of Ni into the bulk lattice. Computationally, the atomic transformations underlying nanoscale alloy restructuring are much more effectively resolved using molecular dynamics (MD). However, these only reveal short time scales up to nanoseconds, not the dynamics occurring over microseconds and beyond that are most significant in alloy surface read less
Funding	Not available

Short KIM ID The unique KIM identifier code.	MO_266134052596_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_FischerSchmitzEich_2019_CuNi__MO_266134052596_000
DOI	10.25950/ca482d5d https://doi.org/10.25950/ca482d5d https://commons.datacite.org/doi.org/10.25950/ca482d5d
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

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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
B The letter grade B was assigned because the normalized error in the computation was 3.01071e-08 compared with a machine precision of 2.22045e-16. The letter grade was based on 'score=log10(error/eps)', with ranges A=[0, 7.5], B=(7.5, 10.0], C=(10.0, 12.5], D=(12.5, 15.0), F>15.0. 'A' is the best grade, and 'F' indicates failure.	vc-forces-numerical-derivative	consistency	Forces computed by the model agree with numerical derivatives of the energy; 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
P No memory leak detected.	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: Cu

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: Ni

Species: Cu

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: Ni

Species: Cu

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: Cu

Species: Ni

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: Cu

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

Species: Cu

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

Species: Cu

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: Cu

Species: Ni

Cubic Crystal Basic Properties Table

Species: Cu

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 Cu v004	view	16400
Cohesive energy versus lattice constant curve for bcc Ni v004	view	19270
Cohesive energy versus lattice constant curve for diamond Cu v004	view	19419
Cohesive energy versus lattice constant curve for diamond Ni v004	view	17481
Cohesive energy versus lattice constant curve for fcc Cu v004	view	19381
Cohesive energy versus lattice constant curve for fcc Ni v004	view	15990
Cohesive energy versus lattice constant curve for sc Cu v004	view	12602
Cohesive energy versus lattice constant curve for sc Ni v004	view	19456

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 Cu at zero temperature v006	view	18062
Elastic constants for bcc Ni at zero temperature v006	view	16978
Elastic constants for fcc Cu at zero temperature v006	view	17972
Elastic constants for fcc Ni at zero temperature v006	view	10362
Elastic constants for sc Cu at zero temperature v006	view	9691
Elastic constants for sc Ni at zero temperature v006	view	9728

EquilibriumCrystalStructure__TD_457028483760_001

Equilibrium structure and energy for a crystal structure at zero temperature and pressure v001

Creators:
Contributor: ilia
Publication Year: 2023
DOI: https://doi.org/10.25950/e8a7ed84

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	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)
Equilibrium crystal structure and energy for Cu in AFLOW crystal prototype A_cF4_225_a v001		view	61473
Equilibrium crystal structure and energy for Cu in AFLOW crystal prototype A_cI2_229_a v001		view	62651

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 Ni in AFLOW crystal prototype A_cF4_225_a v002	view	92467
Equilibrium crystal structure and energy for Ni in AFLOW crystal prototype A_cI2_229_a v002	view	81645
Equilibrium crystal structure and energy for Ni in AFLOW crystal prototype A_hP2_194_c v002	view	81424

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 fcc Cu v001	view	8437232
Relaxed energy as a function of tilt angle for a 100 symmetric tilt grain boundary in fcc Ni v001	view	9274028
Relaxed energy as a function of tilt angle for a 110 symmetric tilt grain boundary in fcc Cu v001	view	27719761
Relaxed energy as a function of tilt angle for a 110 symmetric tilt grain boundary in fcc Ni v001	view	50815117
Relaxed energy as a function of tilt angle for a 111 symmetric tilt grain boundary in fcc Cu v001	view	16028844
Relaxed energy as a function of tilt angle for a 111 symmetric tilt grain boundary in fcc Ni v001	view	26498342
Relaxed energy as a function of tilt angle for a 112 symmetric tilt grain boundary in fcc Cu v001	view	92433852
Relaxed energy as a function of tilt angle for a 112 symmetric tilt grain boundary in fcc Ni v001	view	103695922

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 Cu v007	view	7603
Equilibrium zero-temperature lattice constant for bcc Ni v007	view	8088
Equilibrium zero-temperature lattice constant for diamond Cu v007	view	7678
Equilibrium zero-temperature lattice constant for diamond Ni v007	view	12224
Equilibrium zero-temperature lattice constant for fcc Cu v007	view	12482
Equilibrium zero-temperature lattice constant for fcc Ni v007	view	12373
Equilibrium zero-temperature lattice constant for sc Cu v007	view	7417
Equilibrium zero-temperature lattice constant for sc Ni v007	view	11696

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	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)
Equilibrium lattice constants for hcp Cu v005		view	174871
Equilibrium lattice constants for hcp Ni v005		view	168276

LinearThermalExpansionCoeffCubic__TD_522633393614_001

Linear thermal expansion coefficient of cubic crystal structures v001

Creators: Mingjian Wen
Contributor: mjwen
Publication Year: 2019
DOI: https://doi.org/10.25950/fc69d82d

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	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)
Linear thermal expansion coefficient of fcc Cu at 293.15 K under a pressure of 0 MPa v001		view	17564805

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	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)
Linear thermal expansion coefficient of fcc Ni at 293.15 K under a pressure of 0 MPa v002		view	1286289

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 Cu v004		view	133474
Phonon dispersion relations for fcc Ni v004		view	97459

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 Cu v002		view	14406552
Stacking and twinning fault energies for fcc Ni v002		view	26495286

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	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)
Broken-bond fit of high-symmetry surface energies in fcc Cu v004		view	118078
Broken-bond fit of high-symmetry surface energies in fcc Ni v004		view	167023

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	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)
Monovacancy formation energy and relaxation volume for fcc Cu		view	457331
Monovacancy formation energy and relaxation volume for fcc Ni		view	558117

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	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)
Vacancy formation and migration energy for fcc Cu		view	2240714
Vacancy formation and migration energy for fcc Ni		view	1940268

Errors

ElasticConstantsCubic__TD_011862047401_006

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

PhononDispersionCurve__TD_530195868545_004

Test	Error Categories	Link to Error page
Phonon dispersion relations for fcc Cu v004	other	view
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 fcc Cu 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
MemoryLeak__VC_561022993723_004	other	view
PeriodicitySupport__VC_895061507745_004	other	view

Files

CMakeLists.txt
Cu_Ni_Fischer_2018.eam.alloy
LICENSE
kimprovenance.edn
kimspec.edn

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EAM_Dynamo_FischerSchmitzEich_2019_CuNi__MO_266134052596_000.txz	Tar+XZ	Linux and OS X 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_FischerSchmitzEich_2019_CuNi__MO_266134052596_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

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