EAM potential (LAMMPS cubic hermite tabulation) for the ternary U-Mo-Xe system developed by Smirnova et al. (2013) v005

Alexey Yu Kuksin; Zeke Insepov; Jeff Rest; Abdellatif M. Yacout; Daria Smirnova; Sergey Starikov; Abdellatif M. Yacout

doi:10.25950/4bfd589e

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EAM_Dynamo_SmirnovaKuskinStarikov_2013_UMoXe__MO_679329885632_005

There is a newer version of this item. See EAM_Dynamo_SmirnovaKuskinStarikov_2013_UMoXe__MO_679329885632_006

Interatomic potential for Molybdenum (Mo), Uranium (U), Xenon (Xe).
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Title A single sentence description.	EAM potential (LAMMPS cubic hermite tabulation) for the ternary U-Mo-Xe system developed by Smirnova et al. (2013) v005
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.	EAM potential for U-Mo system with Xe is developed using a force-matching technique and a dataset of ab initio atomic forces. The potential is suitable for the investigation of alloys and compounds existing in the U-Mo system as well as for simulation of pure elements: U, Mo and Xe. Computed lattice constants, thermal expansion coefficients, elastic properties and melting temperatures of U, Mo and Xe are consistent with the experimentally measured values. The energies of the point defect formation in pure U and Mo are proved to be comparable to the density-functional theory calculations.
Species The supported atomic species.	Mo, U, Xe
Disclaimer A statement of applicability provided by the contributor, informing users of the intended use of this KIM Item.	The potential can be used for simulation of the structure and physical properties of BCC U-Mo alloys, nevertheless it gives insufficient description of some characteristics of point defects in these alloys.
Content Origin	http://www.ctcms.nist.gov/potentials/U.html

Contributor	Ryan S. Elliott
Maintainer	Ryan S. Elliott
Developer	Alexey Yu Kuksin Zeke Insepov Jeff Rest Abdellatif M. Yacout Daria Smirnova Sergey Starikov Abdellatif M. Yacout
Published on KIM	2018
How to Cite	Click here to download this 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. 69 Citations (51 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 . W. Zhang, D. Yun, and W. Liu, “Xenon Diffusion Mechanism and Xenon Bubble Nucleation and Growth Behaviors in Molybdenum via Molecular Dynamics Simulations,” Materials. 2019. link Times cited: 10 Abstract: The behaviors of xenon in molybdenum were studied using mole… read more Abstract: The behaviors of xenon in molybdenum were studied using molecular statics and molecular dynamics simulations. The diffusion mechanism of xenon atoms was studied combining molecular dynamics, nudged elastic band, and temperature-accelerated dynamics methods. The vacancy-assisted diffusion mechanism was analyzed and the corresponding energy barriers were calculated. The clustering process of scattered xenon atoms was studied at an elevated temperature. Xenon bubbles were observed to form when the concentration of xenon atoms exceeded a threshold concentration value. Meanwhile, the interaction of xenon bubble and vacancies was investigated via the nudged elastic band method. The results showed that there exists a region around the xenon bubble where the migration energy of vacancy is significantly influenced. This work provides useful insights towards a better understanding of the behaviors of xenon in molybdenum. read less J. J. Moller et al., “110 planar faults in strained bcc metals: Origins and implications of a commonly observed artifact of classical potentials,” Physical Review Materials. 2018. link Times cited: 18 Abstract: Large-scale atomistic simulations with classical potentials … read more Abstract: Large-scale atomistic simulations with classical potentials can provide valuable insights into microscopic deformation mechanisms and defect-defect interactions in materials. Unfortunately, these assets often come with the uncertainty of whether the observed mechanisms are based on realistic physical phenomena or whether they are artifacts of the employed material models. One such example is the often reported occurrence of stable planar faults (PFs) in body-centered cubic (bcc) metals subjected to high strains, e.g., at crack tips or in strained nano-objects. In this paper, we study the strain dependence of the generalized stacking fault energy (GSFE) of {110} planes in various bcc metals with material models of increasing sophistication, i.e., (modified) embedded atom method, angular-dependent, Tersoff, and bond-order potentials as well as density functional theory. We show that under applied tensile strains the GSFE curves of many classical potentials exhibit a local minimum which gives rise to the formation of stable PFs. These PFs do not appear when more sophisticated material models are used and have thus to be regarded as artifacts of the potentials. We demonstrate that the local GSFE minimum is not formed for reasons of symmetry and we recommend including the determination of the strain-dependent (110) GSFE as a benchmark for newly developed potentials. read less S. Hu and B. Beeler, “Gas Bubble Evolution in Polycrystalline UMo Fuels Under Elastic-Plastic Deformation: A Phase-Field Model With Crystal-Plasticity,” Frontiers in Materials. 2021. link Times cited: 4 Abstract: In monolithic UMo fuels, the interaction between the Al clad… read more Abstract: In monolithic UMo fuels, the interaction between the Al cladding and large gas bubble volumetric swelling causes both elastic-plastic and creep deformation. In this work, a phase-field model of gas bubble evolution in polycrystalline UMo under elastic-plastic deformation was developed for studying the dynamic interaction between evolving gas bubble/voids and deformation. A crystal plasticity model, which assumes that the plastic strain rate is proportional to resolved shear stresses of dislocation slip systems on their slip planes, was used to describe plastic deformation in polycrystalline UMo. Xe diffusion and gas bubble evolution are driven by the minimization of chemical and deformation energies in the phase-field model, while evolving gas bubble structure was used to update the mechanical properties in the crystal plasticity model. With the developed model, we simulated the effect of gas bubble structures (different volume fractions and internal gas pressures) on stress-strain curves and the effect of local stresses on gas bubble evolution. The results show that 1) the effective Young’s modulus and yield stress decrease with the increase of gas bubble volume fraction; 2) the hardening coefficient increases with the increase of gas bubble volume fraction, especially for gas bubbles with higher internal pressure; and 3) the pressure dependence of Xe thermodynamic and kinetic properties in addition to the local stress state determine gas bubble growth or shrinkage. The simulated results can serve as a guide to improve material property models for macroscale fuel performance modeling. read less E. Kirova and V. Pisarev, “Morphological aspect of crystal nucleation in wall-confined supercooled metallic film,” Journal of Physics: Condensed Matter. 2020. link Times cited: 1 Abstract: In this paper, we simulate the nucleation and growth of crys… read more Abstract: In this paper, we simulate the nucleation and growth of crystalline nuclei in a molybdenum film cooled at different rates confined between two amorphous walls. We also compare the results for the wall-confined and wall-free systems. We apply the same methodology as in the work (Kirova and Pisarev 2019 J. Cryst. Growth 528 125266) which is based on reconstructing the probability density function for the largest crystalline nucleus in the system. The size of the nucleus and the asphericity parameter are considered as the reaction coordinates. We demonstrate that in both the free and confined systems there are two mechanisms of crystal growth: the attachment of atoms to the biggest crystal from the amorphous phase and the merging of the biggest crystal cluster with small ones (coalescence). We show that the attachment mechanism is dominant in the melt cooled down at a slower rate, and the mechanism gradually shifts to coalescence as cooling rate increases. We also observe the formation of long-lived crystal clusters and demonstrate that amorphous walls do not affect their geometric characteristics. However, system confined between walls demonstrates higher glass-forming ability. read less S. Kim, K. Kang, and S. Y. Kim, “Dynamic drags acting on moving defects in discrete dispersive media: From dislocation to low-angle grain boundary,” arXiv: Materials Science. 2019. link Times cited: 6 X. Chen, R. Huang, T.-M. Shih, and Y. Wen, “Shape Stability of Metallic Nanoplates: A Molecular Dynamics Study,” Nanoscale Research Letters. 2019. link Times cited: 3 D. Yun et al., “Characterization of high energy Xe ion irradiation effects in single crystal molybdenum with depth-resolved synchrotron microbeam diffraction,” Journal of Nuclear Materials. 2016. link Times cited: 9 Z. Shi and C. V. Singh, “Competing twinning mechanisms in body-centered cubic metallic nanowires,” Scripta Materialia. 2016. link Times cited: 39 N. Wang et al., “Embedded atom model for the liquid U–10Zr alloy based on density functional theory calculations,” RSC Advances. 2015. link Times cited: 5 Abstract: Understanding the structural characteristics of U–Zr alloys … read more Abstract: Understanding the structural characteristics of U–Zr alloys is significant because they are very promising candidates for core materials in Gen IV nuclear reactors. In this work, we developed an embedded atom model for the liquid U–10Zr alloy based on density functional theory calculations and studied the structural, thermodynamic, and atomic-transport characteristics of liquid U–Zr alloys by molecular dynamics simulations based on this embedded atom model. The results showed that the mixed volume of U and Zr increased and the formation enthalpy of mixing was positive. The self-diffusion in liquid U–10Zr alloy obeyed the Arrhenius activation process; linear correlation coefficients were about 0.98 for the Arrhenius plots. The activation energies were evaluated as 33.9 and 32.6 kJ mol−1 and the preexponential factors at 23.73 and 21.88 × 10−5 cm2 s−1 for U and Zr, respectively. read less D. Belashchenko, “Computer simulation of liquid metals,” Physics—Uspekhi. 2013. link Times cited: 84 Abstract: Methods for and the results of the computer simulation of li… read more Abstract: Methods for and the results of the computer simulation of liquid metals are reviewed. Two basic methods, classical molecular dynamics with known interparticle potentials and the ab initio method, are considered. Most attention is given to the simulated results obtained using the embedded atom model (EAM). The thermodynamic, structural, and diffusion properties of liquid metal models under normal and extreme (shock) pressure conditions are considered. Liquid-metal simulated results for the Groups I–IV elements, a number of transition metals, and some binary systems (Fe–C, Fe–S) are examined. Possibilities for the simulation to account for the thermal contribution of delocalized electrons to energy and pressure are considered. Solidification features of supercooled metals are also discussed. read less B. Beeler and Y. Zhang, “The reconciliation and validation of a combined interatomic potential for the description of Xe in γU-Mo,” Frontiers in Nuclear Engineering. 2023. link Times cited: 0 Abstract: A U-Mo alloy has been selected as the fuel design for the co… read more Abstract: A U-Mo alloy has been selected as the fuel design for the conversion of high-performance research reactors in the United States. Efforts are ongoing to describe the fuel evolution as a function of time, for a variety of different reactor conditions. The accurate prediction of fuel evolution under irradiation requires the implementation of correct thermodynamic properties into mesoscale and continuum-level fuel performance modeling codes. Molecular dynamics has proven to be a valuable tool to parameterize or inform these higher-length scale models. However, there are currently inaccuracies in the only available U-Mo-Xe potential, which limits the predictive capabilities of molecular dynamics to inform critical phenomena in these fuel systems such as fission gas swelling. This work provides an updated U-Mo-Xe ternary interatomic potential which combines existing potentials in a reconciled format. The validation of the interatomic potential is performed by analyzing the phase stability and vacancy formation energies. Subsequently, Xe solution energies and an equation of state to describe Xe bubbles in U-Mo are calculated, providing 1) evidence of the significant differences between the prior ternary potential and the currently presented potential, and 2) updated data/tools for implementation into mesoscale simulation methodologies to study fission gas bubble evolution. read less A. Ullah, Q. Wang, and Y. Song, “Kinetics of substitutional Xe and self-interstitial Mo in γ U–10Mo: a molecular dynamic study,” Radiation Effects and Defects in Solids. 2023. link Times cited: 0 Abstract: Diffusion is one among multiple reasons responsible for the … read more Abstract: Diffusion is one among multiple reasons responsible for the micro-structural changes instigating substantial alterations in the macroscopic properties of materials. Simulating irradiation effects offers necessary insights into the production of radiation damages and their impact on different characteristics of nuclear fuels. Atomistic simulations were performed to obtain the migration energy and diffusion pre-factor of a single Xenon (Xe) substitutional as a function of percent atomic vacancies concentration in γ Uranium 10 wt% Molybdenum (γ U–10Mo). We observed a decrease and an increase in the migration energy and diffusion pre-factor respectively with an increase in the population of vacancies which proved that vacancy diffusion is a true mechanism of Xe in metals. Nudged elastic band (NEB) calculations were involved to obtain the migration energy of a self-interstitial Mo for intra- and inter-planar transitions. We observed the migration energy of Mo substantially higher than that of Xe which indicated that Xe is more mobile than Mo in bcc γ U–10Mo. read less P. Söderlind, A. Landa, E. Moore, A. Perron, J. Roehling, and J. McKeown, “High-Temperature Thermodynamics of Uranium from Ab Initio Modeling,” Applied Sciences. 2023. link Times cited: 0 Abstract: We present high-temperature thermodynamic properties for ura… read more Abstract: We present high-temperature thermodynamic properties for uranium in its γ phase (γ-U) from first-principles, relativistic, and anharmonic theory. The results are compared to CALPHAD modeling. The ab initio electronic structure is obtained from density-functional theory (DFT) that includes spin–orbit coupling and an added self-consistent orbital-polarization (OP) mechanism for more accurate treatment of magnetism. The first-principles method is coupled to a lattice dynamics scheme that is used to model anharmonic lattice vibrations, namely, Self-Consistent Ab Initio Lattice Dynamics (SCAILD). The methodology can be summarized in the acronym DFT + OP + SCAILD. Upon thermal expansion, γ-U develops non-negligible magnetic moments that are included for the first time in thermodynamic theory. The all-electron DFT approach is shown to model γ-U better than the commonly used pseudopotential method. In addition to CALPHAD, DFT + OP + SCAILD thermodynamic properties are compared with other ab initio and semiempirical modeling and experiments. Our first-principles approach produces Gibbs free energy that is essentially identical to CALPHAD. The DFT + OP + SCAILD heat capacity is close to CALPHAD and most experimental data and is predicted to have a significant thermal dependence due to the electronic contribution. read less E. A. Lobashev, A. Antropov, and V. Stegailov, “Formation, Diffusion, and Growth of Gas Bubbles in γ-Uranium with the Excess of Interstitial Atoms: Relation between Molecular Dynamics and Kinetics,” Journal of Experimental and Theoretical Physics. 2023. link Times cited: 0 S. Starikov and D. Smirnova, “Details of structure transformations in pure uranium and U-Mo alloys: insights from classical atomistic simulation,” Journal of Nuclear Materials. 2023. link Times cited: 1 G. Park, B. Beeler, and M. Okuniewski, “Computational determination of a primary diffusion mode in γU-10Mo under irradiation,” Journal of Nuclear Materials. 2022. link Times cited: 2 A. Ullah, Q. Wang, and Y. Song, “Dynamic and kinetic properties of point defects in γ U-10Mo: a molecular dynamics study,” Radiation Effects and Defects in Solids. 2022. link Times cited: 1 Abstract: Controlled defects population and their movement constitute … read more Abstract: Controlled defects population and their movement constitute the foundation for describing micro-structural evolution in any material systems for nuclear applications. Molecular dynamics (MD) simulations were performed to study temperature (1200–1400 K) dependent displacement cascades with a U primary knock on atom (pka) carrying 2 keV kinetic energy in γ U-10Mo. We observed a strong impact of the temperature on the population of point defects. The impact of kinetic energy of the pka on the number of survived and maximum point defects is addressed carefully where an increase in the number of defects led to an increase in the energy. Nudged elastic band (NEB) calculations were involved to calculate the activation energies of self-interstitial Mo for different intra and inter-planar transitions. The transitions between 〈011〉 to 〈011〉, 〈001〉 to 〈011〉, 〈001〉 to 〈111〉, 〈111〉 to 〈001〉 and 〈111〉 to 〈110〉 Mo-Mo dumbbell configurations were explored. The impact of applied strains on the activation energy in each case of the transitions was investigated where irregular behavior in the energy with rise in the strain was observed. read less J. French and X. Bai, “Molecular Dynamics Studies of Grain Boundary Mobility and Anisotropy in BCC γ-Uranium,” Journal of Nuclear Materials. 2022. link Times cited: 3 A. R. M. Iasir and K. Hammond, “Xenon mobility in γ-uranium and uranium–molybdenum alloys,” Journal of Applied Physics. 2022. link Times cited: 5 A. Nazarov et al., “The cluster species effect on the noble gas cluster interaction with solid surfaces,” Surfaces and Interfaces. 2021. link Times cited: 4 A. H. M. Faisal and C. Weinberger, “Modeling twin boundary structures in body centered cubic transition metals,” Computational Materials Science. 2021. link Times cited: 6 A. Ullah, M. Ado, and S. Adede, “Effect of Concentration of Mo on The Mechanical Behavior of UMo Fuel: An Atomistic Study.” 2021. link Times cited: 0 Abstract: We performed molecular dynamics simulation on nanoindentat… read more Abstract: We performed molecular dynamics simulation on nanoindentation of Uranium Molybdenum alloys using spherical indenter. A ternary potential developed for UMoXe was utilized. We calculated the updated values for hardness and reduced elastic modulus at different concentrations of Mo. The whole process of deformation and dislocation analysis was visualized using OVITO. We found an increase in deformation with increase in stress while dislocations are not found anyhow induced defects have been distributed throughout the simulation cell randomly. The increase in concentration affected the hardness and reduced elastic modulus significantly. read less D. Vizoso and C. Deo, “Determination of Vacancy Formation Energies in Binary UZr Alloys Using Special Quasirandom Structure Methods,” Frontiers in Materials. 2021. link Times cited: 4 Abstract: The use of predictive models to examine defect production an… read more Abstract: The use of predictive models to examine defect production and migration in metallic systems requires a thorough understanding of the energetics of defect formation and migration. In fully miscible alloys, atomistic properties will all have a range of values that are heavily dependent on local atomic configurations. In this work we have used the atomistic simulation tool Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) to investigate the impact of first nearest neighbor configuration on vacancy formation energies at 0 K in γ-U-Zr alloys of varying Zr concentrations. The properties of randomly generated alloy microstructures were also compared with those produced as special quasi-random structures (SQS) using the “mcsqs” code within the Alloy Theoretic Automated Toolkit. Results have confirmed that local configuration can have a significant impact on measured properties and must be considered when characterizing miscible alloy systems. Results also indicated that the generation method of the random structure (i.e., via random species assignment or a method of enforced randomness) does not result in a measurable difference in average vacancy formation energies in miscible U-Zr systems. read less G. Park, B. Beeler, and M. Okuniewski, “An atomistic study of defect energetics and diffusion with respect to composition and temperature in γU and γU-Mo alloys,” Journal of Nuclear Materials. 2021. link Times cited: 10 C. Wen et al., “Combined EXAFS and MD study to elucidate defect behaviors and stage IV recovery mechanism in heavy ion irradiated molybdenum,” Materialia. 2021. link Times cited: 0 M. Jin, Y. Gao, C. Jiang, and J. Gan, “Defect dynamics in γ-U, Mo, and their alloys,” Journal of Nuclear Materials. 2021. link Times cited: 3 D. Yun, J. Rest, W. Zhang, X. Xie, W. Liu, and L. Gu, “A quantitative analysis of an in situ Xe ion implantation experiment on single crystal molybdenum via a molecular dynamics informed kinetic rate theory simulation,” Journal of Nuclear Materials. 2020. link Times cited: 2 Y. Chen, X. Liao, N. Gao, W. Hu, F. Gao, and H. Deng, “Interatomic potentials of W–V and W–Mo binary systems for point defects studies,” Journal of Nuclear Materials. 2020. link Times cited: 13 S. Kim, H. Kim, K. Kang, and S. Y. Kim, “Relativistic effect inducing drag on fast-moving dislocation in discrete system,” International Journal of Plasticity. 2020. link Times cited: 19 B. Beeler, S. Hu, Y. Zhang, and Y. Gao, “A improved equation of state for Xe gas bubbles in γU-Mo fuels,” Journal of Nuclear Materials. 2020. link Times cited: 12 D. Smirnova et al., “Atomistic description of self-diffusion in molybdenum: A comparative theoretical study of non-Arrhenius behavior,” Physical Review Materials. 2020. link Times cited: 16 Abstract: According to experimental observations, the temperature depe… read more Abstract: According to experimental observations, the temperature dependence of self-diffusion coefficient in most body-centered cubic metals (bcc) exhibits non-Arrhenius behavior. The origin of this behavio ... read less I. Kruglov, A. Yanilkin, A. Oganov, and P. Korotaev, “Phase diagram of uranium from ab initio calculations and machine learning,” Physical Review B. 2019. link Times cited: 21 Abstract: Ivan A. Kruglov ,1,2,* Alexey Yanilkin,1,2 Artem R. Oganov,3… read more Abstract: Ivan A. Kruglov ,1,2,* Alexey Yanilkin,1,2 Artem R. Oganov,3,2,1 and Pavel Korotaev1,4 1Dukhov Research Institute of Automatics (VNIIA), Moscow 127055, Russian Federation 2Moscow Institute of Physics and Technology, Dolgoprudny 141700, Russian Federation 3Skolkovo Institute of Science and Technology, Skolkovo Innovation Center, Moscow 143026, Russian Federation 4Material Modeling and Development Laboratory, NUST MISiS, Moscow 119991, Russian Federation read less I. Kruglov et al., “Machine Learning Interatomic Potentials for Global Optimization and Molecular Dynamics Simulation,” Materials Informatics. 2019. link Times cited: 3 J. Chen, W. Ouyang, W. Lai, J. Li, and Z. Zhang, “A new type angular-dependent interatomic potential and its application to model displacement cascades in uranium,” Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms. 2019. link Times cited: 5 B. Beeler, Y. Zhang, M. Okuniewski, and C. Deo, “Calculation of the displacement energy of α and γ uranium,” Journal of Nuclear Materials. 2018. link Times cited: 18 B. Beeler, M. Baskes, D. Andersson, M. Cooper, and Y. Zhang, “A modified Embedded-Atom Method interatomic potential for uranium-silicide,” Journal of Nuclear Materials. 2017. link Times cited: 26 I. Kruglov, O. Sergeev, A. Yanilkin, and A. Oganov, “Energy-free machine learning force field for aluminum,” Scientific Reports. 2017. link Times cited: 51 X. Tian, D. Li, Y. Yu, Z. You, T. Li, and L. Ge, “Atomistic simulation study of deformation twinning of nanocrystalline body-centered cubic Mo,” Materials Science and Engineering A-structural Materials Properties Microstructure and Processing. 2017. link Times cited: 16 K. Fidanyan and V. Stegailov, “Vibrational properties of bcc U and Mo at different temperatures,” Journal of Physics: Conference Series. 2016. link Times cited: 2 Abstract: An accurate description of the vibrational density of states… read more Abstract: An accurate description of the vibrational density of states is required for calculation of thermodynamic properties of crystal lattices as well as defect migration rates in the Vineyard theory framework. In this work we use EAM and MEAM interatomic potentials and apply the zero temperature lattice dynamics calculations and the velocity autocorrelation function calculations at finite temperatures to study vibrational density of states in bcc molybdenum and bcc uranium lattices. The latter case is especially interesting since γ-U is thermodynamically not favorable at low temperatures and is not observed in experiments. read less J. Zheng, X. Tian, L. Shao, X. Pan, P. Tang, and B. Y. Tang, “Point defects and Zn-doping in defective Laves phase C15 MgCu2: A first-principles study,” Computational Materials Science. 2016. link Times cited: 9 S. Starikov and L. Kolotova, “Features of cubic and tetragonal structures of U–Mo alloys: Atomistic simulation,” Scripta Materialia. 2016. link Times cited: 8 A. P. Moore, B. Beeler, C. Deo, M. Baskes, and M. Okuniewski, “Atomistic modeling of high temperature uranium–zirconium alloy structure and thermodynamics,” Journal of Nuclear Materials. 2015. link Times cited: 41 E. S. Wise, M. Liu, and T. Miller, “Sputtering of cubic metal crystals by low-energy xenon-ions,” Computational Materials Science. 2015. link Times cited: 5 H. Xiao, C. Long, X. Tian, and S. Li, “Atomistic simulations of the small xenon bubble behavior in U–Mo alloy,” Materials & Design. 2015. link Times cited: 16 D. Smirnova, A. Kuksin, and S. Starikov, “Investigation of point defects diffusion in bcc uranium and U–Mo alloys,” Journal of Nuclear Materials. 2015. link Times cited: 47 X. Tian, H. Xiao, R. Tang, and C.-H. Lu, “Molecular dynamics simulation of displacement cascades in U–Mo alloys,” Nuclear Instruments & Methods in Physics Research Section B-beam Interactions With Materials and Atoms. 2014. link Times cited: 11 D. E. Smirnova, S. Starikov, S. Starikov, V. Stegailov, and V. Stegailov, “Interatomic potential for uranium in a wide range of pressures and temperatures,” Journal of Physics: Condensed Matter. 2012. link Times cited: 3 Abstract: Using the force-matching method we develop an interatomic po… read more Abstract: Using the force-matching method we develop an interatomic potential that allows us to study the structure and properties of α-U, γ-U and liquid uranium. The potential is fitted to the forces, energies and stresses obtained from ab initio calculations. The model gives a good comparison with the experimental and ab initio data for the lattice constants of α-U and γ-U, the elastic constants, the room-temperature isotherm, the normal density isochore, the bond-angle distribution functions and the vacancy formation energies. The calculated melting line of uranium at pressures up to 80 GPa and the temperature of the α–γ transition at 3 GPa agree well with the experimental phase diagram of uranium. read less A. A. Saltos, N. Peters, and K. Hammond, “Crystal binding effects on neutron scattering and criticality in U–Mo fuels,” Journal of Nuclear Materials. 2021. link Times cited: 0 Y. Zhang, Z. Xiao, and X. Bai, “Effect of Cr Concentration on ½<111> to <100> Dislocation Loop Transformation in Fe-Cr alloys,” Journal of Nuclear Materials. 2021. link Times cited: 10 B. Beeler, M. Cooper, Z. Mei, D. Schwen, and Y. Zhang, “Radiation driven diffusion in γU-Mo,” Journal of Nuclear Materials. 2021. link Times cited: 12 N. Kondratyuk, G. Norman, and V. Stegailov, “Quantum nuclear effects in water using centroid molecular dynamics,” Journal of Physics: Conference Series. 2018. link Times cited: 4 Abstract: The quantum nuclear effects are studied in water using the m… read more Abstract: The quantum nuclear effects are studied in water using the method of centroid molecular dynamics (CMD). The aim is the calibration of CMD implementation in LAMMPS. The calculated intramolecular energy, atoms gyration radii and radial distribution functions are shown in comparison with previous works. The work is assumed to be the step toward to solution of the discrepancy between the simulation results and the experimental data of liquid n-alkane properties in our previous works. read less J. Janssen et al., “pyiron: An integrated development environment for computational materials science,” Computational Materials Science. 2019. link Times cited: 60 Y. Li, “A universal COMB potential for the whole composition range of the uranium oxygen system,” Journal of Nuclear Materials. 2019. link Times cited: 6 G. Norman et al., “What for and which exaflops supercomputers are necessary in natural sciences.” 2015. link Times cited: 3 W. Ouyang, W. Lai, J. Li, J.-bo Liu, and B.-xin Liu, “Atomic Simulations of U-Mo under Irradiation: A New Angular Dependent Potential,” Metals. 2021. link Times cited: 4 Abstract: Uranium-Molybdenum alloy has been a promising option in the … read more Abstract: Uranium-Molybdenum alloy has been a promising option in the production of metallic nuclear fuels, where the introduction of Molybdenum enhances mechanical properties, corrosion resistance, and dimensional stability of fuel components. Meanwhile, few potential options for molecular dynamics simulations of U and its alloys have been reported due to the difficulty in the description of the directional effects within atomic interactions, mainly induced by itinerant f-electron behaviors. In the present study, a new angular dependent potential formalism proposed by the author’s group has been further applied to the description of the U-Mo systems, which has achieved a moderately well reproduction of macroscopic properties such as lattice constants and elastic constants of reference phases. Moreover, the potential has been further improved to more accurately describe the threshold displacement energy surface at intermediate and short atomic distances. Simulations of primary radiation damage in solid solutions of the U-Mo system have also been carried out and an uplift in the residual defect population has been observed when the Mo content decreases to around 5 wt.%, which corroborates the negative role of local Mo depletion in mitigation of irradiation damage and consequent swelling behavior. read less L. Lang et al., “Development of a Ni–Mo interatomic potential for irradiation simulation,” Modelling and Simulation in Materials Science and Engineering. 2019. link Times cited: 5 Abstract: An interatomic potential for the Ni–Mo binary alloy focusing… read more Abstract: An interatomic potential for the Ni–Mo binary alloy focusing on irradiation has been constructed with the modified analysis embedded atom method. The newly developed interatomic (Ni–Ni and Mo–Mo) potentials and the Ni–Mo cross-interactions are fitted to the ab initio results and experimental data, including defect energies, formation energies of three stable phases. The properties used for fitting are accurately reproduced by the present potentials for both pure elements and alloy systems. Those properties beyond the fitting ranges are also well predicted, demonstrating its excellent transferability. The advantages and certain weaknesses of the new potential are also discussed in detail compared with other existing potentials. The potential is expected to be especially suitable for irradiation simulations of Ni–Mo alloys. read less G. Smirnov, G. Smirnov, V. Stegailov, and V. Stegailov, “Formation free energies of point defects and thermal expansion of bcc U and Mo,” Journal of Physics: Condensed Matter. 2019. link Times cited: 9 Abstract: -U is a high temperature body-centred cubic (bcc) phase of u… read more Abstract: -U is a high temperature body-centred cubic (bcc) phase of uranium which is mechanically unstable at T = 0 K. The point defect properties in pure bcc uranium are not sufficiently well studied. In this work we use classical molecular dynamics simulations with the thermodynamic integration approach to calculate the formation free energies of vacancies and interstitials in bcc uranium and, for comparison, in bcc molybdenum. Contrary to the majority of other metals where the formation free energy is (much) higher for interstitials than for vacancies, our results show that in -uranium interstitials are the dominating type of defects in thermal equilibrium. We discuss the possible implications of this finding in the context of the thermal expansion data for -U that provide a certain supporting evidence. read less Y. Lysogorskiy, T. Hammerschmidt, J. Janssen, J. Neugebauer, and R. Drautz, “Transferability of interatomic potentials for molybdenum and silicon,” Modelling and Simulation in Materials Science and Engineering. 2019. link Times cited: 14 Abstract: Interatomic potentials are widely used in computational mate… read more Abstract: Interatomic potentials are widely used in computational materials science, in particular for simulations that are too computationally expensive for density functional theory (DFT). Most interatomic potentials have a limited application range and often there is very limited information available regarding their performance for specific simulations. We carried out high-throughput calculations for molybdenum and silicon with DFT and a number of interatomic potentials. We compare the DFT reference calculations and experimental data to the predictions of the interatomic potentials. We focus on a large number of basic materials properties, including the cohesive energy, atomic volume, elastic coefficients, vibrational properties, thermodynamic properties, surface energies and vacancy formation energies, which enables a detailed discussion of the performance of the different potentials. We further analyze correlations between properties as obtained from DFT calculations and how interatomic potentials reproduce these correlations, and suggest a general measure for quantifying the accuracy and transferability of an interatomic potential. From our analysis we do not establish a clearcut ranking of the potentials as each potential has its strengths and weaknesses. It is therefore essential to assess the properties of a potential carefully before application of the potential in a specific simulation. The data presented here will be useful for selecting a potential for simulations of Mo or Si. read less I. Novoselov et al., “Moment tensor potentials as a promising tool to study diffusion processes,” Computational Materials Science. 2018. link Times cited: 64 A. A. Saltos, N. Peters, and K. Hammond, “Thermal neutron scattering cross sections of 238U and 235U in the γ phase,” Journal of Physics: Condensed Matter. 2018. link Times cited: 4 Abstract: The development of metallic, low-enrichment uranium fuels re… read more Abstract: The development of metallic, low-enrichment uranium fuels requires accurate prediction of their neutron transport properties and reactivity parameters, which in turn require thermal neutron scattering data. Accurate prediction of thermal neutron scattering data, including thermal cross sections, requires knowledge of the phonon scattering properties of the medium, but such matrix binding effects in next-generation fuels such as U–Mo, U–Zr, and U–Si are typically neglected because these effects are often difficult to measure or calculate. Using molecular dynamics simulations with previously published interatomic potentials, we calculate the phonon dispersion relations and phonon densities of states for 235U and 238U in the α and γ phases. The performance of these potentials was evaluated using published ab initio simulation data and inelastic neutron scattering data. The phonon densities of states obtained by each potential were then utilized to calculate the thermal neutron scattering cross sections of 235U and 238U at 1113 K using the NJOY program. The resulting thermal neutron scattering cross sections are assessed by comparison to data obtained from available experimental densities of states. The cross sections generated show how the addition of binding effects decreases the cross section by up to a factor of six over the free-atom model. A definite effect on reactivity is also demonstrated by the use of these thermal libraries on a simple core model. As a consequence, the cross sections generated in this work provide a better description of the true cross section than the free-atom data currently available. We also discuss the sensitivity of the thermal scattering cross sections to the phonon density of states. read less S. Hu, D. Burkes, C. Lavender, D. Senor, W. Setyawan, and Z. Xu, “Formation mechanism of gas bubble superlattice in UMo metal fuels: Phase-field modeling investigation,” Journal of Nuclear Materials. 2016. link Times cited: 54 Y. Li, A. Chernatynskiy, J. Kennedy, S. Sinnott, and S. Phillpot, “Lattice expansion by intrinsic defects in uranium by molecular dynamics simulation,” Journal of Nuclear Materials. 2016. link Times cited: 13 G. Smirnov and V. Stegailov, “Anomalous diffusion of guest molecules in hydrogen gas hydrates,” High Temperature. 2015. link Times cited: 10 D. Smirnova et al., “Atomistic modeling of the self-diffusion in γ-U and γ-U-Mo,” The Physics of Metals and Metallography. 2015. link Times cited: 31 S. Starikov, “Atomistic simulation of the process of defect formation in uranium dioxide during fission fragments flying through,” High Temperature. 2015. link Times cited: 8 X. Hongxing, T. Rui, T. Xiao-feng, and L. Chongsheng, “Molecular Dynamics Simulation of Xe Behavior in U-Mo Alloys Fuel,” Chinese Physics Letters. 2014. link Times cited: 3 Abstract: Classical molecular dynamics simulations are used to investi… read more Abstract: Classical molecular dynamics simulations are used to investigate the fission gas Xe behavior in a U-Mo alloy fuel matrix. The embedded atom method potential proposed by Smirnova et al. is used to describe the U-Mo-Xe system. The results show that the initial configuration of interstitial Xe atoms in U-Mo alloys is very instable and has a strong tendency to get together and to form a Xe bubble by ejecting the adjacent U atoms and Mo atoms from their former normal lattice sites. The pressure in Xe bubbles is initially quite high and then drops with increasing Xe concentration obviously. The matrix swelling of U-Mo alloys associated with the Xe bubble growth follows approximately a linear relationship with the ratio of Xe to U at low Xe concentration while the rate of swelling increases rapidly at high Xe concentration. The simulation results are in good agreement with the experimental data. The recovery of the damaged structure in the U-Mo alloys matrix is also investigated. It is shown that a damaged structure cannot be recovered completely after a system is relaxed for a long time while still having lots of defects. read less С. Волегов, Р. М. Герасимов, and Р. П. Давлятшин, “MODELS OF MOLECULAR DYNAMICS: A REVIEW OF EAM-POTENTIALS. PART 2. POTENTIALS FOR MULTI-COMPONENT SYSTEMS.” 2018. link Times cited: 1 Abstract: Получена: 18 мая 2018 г. Принята: 25 июня 2018 г. Опубликова… read more Abstract: Получена: 18 мая 2018 г. Принята: 25 июня 2018 г. Опубликована: 29 июня 2018 г. В статье представлена вторая часть обзора современных подходов и работ, посвященных построению потенциалов межатомного взаимодействия с использованием методологии погруженного атома (EAM-потенциалы). Эта часть обзора посвящена одной из наиболее остро стоящих проблем в молекулярной динамике – вопросам построения потенциалов, которые были бы пригодны для описания структуры и физико-механических свойств многокомпонентных (в первую очередь – бинарных и тернарных) материалов. Отмечены первые работы, в которых предлагались подходы к построению функций перекрестного взаимодействия для сплавов никеля и меди – как с использованием методологии EAM, так и несколько отличающийся по процедуре построения потенциал типа Финисса-Синклера. Рассматриваются работы, в которых производится сопоставление различных подходов к построению потенциалов, а также к процедуре идентификации их параметров на примере одних и тех же многокомпонентных систем (типа Al-Ni или Cu-Au). Кроме того, особый интерес представляют некоторые тернарные системы, например Fe–Ni–Cr, W–H– He или U–Mo–Xe, которые являются ключевыми для материалов атомной энергетики и которые в последние годы активно изучаются как возможные материалы для использования в термоядерных ректорах. Приведены примеры работ, в которых предлагаются и исследуются потенциалы для описания многокомпонентных систем, пригодных для использования в аэрокосмической промышленности и изготовленных прежде всего на основе никеля. Рассмотрены результаты исследований различных интерметаллических соединений, отмечены работы, в которых при помощи построенного EAM потенциала удалось количественно точно описать фазовые диаграммы соединений и вычислить характеристики фазовых переходов. read less A. P. Moore, C. Deo, M. Baskes, M. Okuniewski, and D. McDowell, “Understanding the uncertainty of interatomic potentials’ parameters and formalism,” Computational Materials Science. 2017. link Times cited: 17 P. Brommer, A. Kiselev, D. Schopf, P. Beck, J. Roth, and H. Trebin, “Classical interaction potentials for diverse materials from ab initio data: a review of potfit,” Modelling and Simulation in Materials Science and Engineering. 2014. link Times cited: 76 Abstract: Force matching is an established technique to generate effec… read more Abstract: Force matching is an established technique to generate effective potentials for molecular dynamics simulations from first-principles data. This method has been implemented in the open source code potfit. Here, we present a review of the method and describe the main features of the code. Particular emphasis is placed on the features added since the initial release: interactions represented by analytical functions, differential evolution as optimization method, and a greatly extended set of interaction models. Beyond the initially present pair and embedded-atom method potentials, potfit can now also optimize angular dependent potentials, charge and dipolar interactions, and electron-temperature-dependent potentials. We demonstrate the functionality of these interaction models using three example systems: phonons in type I clathrates, fracture of α-alumina, and laser-irradiated silicon. read less
Funding	Not available

Short KIM ID The unique KIM identifier code.	MO_679329885632_005
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_SmirnovaKuskinStarikov_2013_UMoXe__MO_679329885632_005
DOI	10.25950/4bfd589e https://doi.org/10.25950/4bfd589e https://commons.datacite.org/doi.org/10.25950/4bfd589e
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	2.0
Potential Type	eam

Previous Version	EAM_Dynamo_SmirnovaKuskinStarikov_2013_UMoXe__MO_679329885632_004

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 letter grade F was assigned because the normalized error in the computation was 1.54829e+02 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^-1 continuous. This means that the model has discontinuous energy.	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: Mo

Species: U

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: U

Species: Mo

Species: Xe

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: Mo

Species: Xe

Species: U

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: Mo

Species: Xe

Species: U

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: U

Species: Mo

Species: Xe

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: Xe

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: Xe

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: U

Species: Xe

Species: Mo

Cubic Crystal Basic Properties Table

Species: Mo

Species: U

Species: Xe

Tests

Disclaimer From Model Developer

The potential can be used for simulation of the structure and physical properties of BCC U-Mo alloys, nevertheless it gives insufficient description of some characteristics of point defects in these alloys.

CohesiveEnergyVsLatticeConstant__TD_554653289799_002

Cohesive energy versus lattice constant curve for monoatomic cubic lattices v002

Creators: Daniel S. Karls
Contributor: karls
Publication Year: 2018
DOI: https://doi.org/10.25950/c6746c52

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	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)
Cohesive energy versus lattice constant curve for bcc Xenon		view	1723

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 Mo v004	view	3690
Cohesive energy versus lattice constant curve for bcc U v004	view	3849
Cohesive energy versus lattice constant curve for diamond Mo v004	view	4227
Cohesive energy versus lattice constant curve for diamond U v004	view	4785
Cohesive energy versus lattice constant curve for diamond Xe v004	view	4933
Cohesive energy versus lattice constant curve for fcc Mo v004	view	3988
Cohesive energy versus lattice constant curve for fcc U v004	view	4638
Cohesive energy versus lattice constant curve for fcc Xe v004	view	4277
Cohesive energy versus lattice constant curve for sc Mo v004	view	4622
Cohesive energy versus lattice constant curve for sc U v004	view	4491
Cohesive energy versus lattice constant curve for sc Xe v004	view	4785

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	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)
Elastic constants for U in AFLOW crystal prototype A_oC4_63_c at zero temperature and pressure v000		view	94455

ElasticConstantsCubic__TD_011862047401_004

Elastic constants for cubic crystals at zero temperature and pressure v004

Creators: Junhao Li
Contributor: jl2922
Publication Year: 2018
DOI: https://doi.org/10.25950/75393d88

Computes the cubic elastic constants for some common crystal types (fcc, bcc, sc) 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	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)
Elastic constants for bcc Xe at zero temperature		view	2346

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 Mo at zero temperature v006	view	6110
Elastic constants for bcc U at zero temperature v006	view	1951
Elastic constants for diamond U at zero temperature v001	view	17850
Elastic constants for diamond Xe at zero temperature v001	view	5790
Elastic constants for fcc Mo at zero temperature v006	view	1983
Elastic constants for fcc U at zero temperature v006	view	2239
Elastic constants for fcc Xe at zero temperature v006	view	3967
Elastic constants for sc Mo at zero temperature v006	view	1535
Elastic constants for sc U at zero temperature v006	view	1567
Elastic constants for sc Xe at zero temperature v006	view	1376

ElasticConstantsHexagonal__TD_612503193866_004

Elastic constants for hexagonal crystals at zero temperature v004

Creators: Junhao Li
Contributor: jl2922
Publication Year: 2019
DOI: https://doi.org/10.25950/d794c746

Computes the elastic constants for hcp crystals 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	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)
Elastic constants for hcp Mo at zero temperature v004		view	1528
Elastic constants for hcp U at zero temperature v004		view	1464

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 Mo in AFLOW crystal prototype A_cF4_225_a v002	view	53833
Equilibrium crystal structure and energy for U in AFLOW crystal prototype A_cF4_225_a v002	view	70846
Equilibrium crystal structure and energy for Xe in AFLOW crystal prototype A_cF4_225_a v002	view	69449
Equilibrium crystal structure and energy for Mo in AFLOW crystal prototype A_cI2_229_a v002	view	55595
Equilibrium crystal structure and energy for U in AFLOW crystal prototype A_cI2_229_a v002	view	84664
Equilibrium crystal structure and energy for Mo in AFLOW crystal prototype A_hP1_191_a v002	view	49398
Equilibrium crystal structure and energy for Xe in AFLOW crystal prototype A_hP2_194_c v002	view	81056
Equilibrium crystal structure and energy for Mo in AFLOW crystal prototype A_hP4_194_ac v002	view	39798
Equilibrium crystal structure and energy for U in AFLOW crystal prototype A_oC4_63_c v002	view	76801
Equilibrium crystal structure and energy for MoU in AFLOW crystal prototype AB2_tI6_139_a_e v002	view	43626

GrainBoundaryCubicCrystalSymmetricTiltRelaxedEnergyVsAngle__TD_410381120771_002

Relaxed energy as a function of tilt angle for a symmetric tilt grain boundary within a cubic crystal v002

Creators: Brandon Runnels
Contributor: brunnels
Publication Year: 2019
DOI: https://doi.org/10.25950/4723cee7

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 110 symmetric tilt grain boundary in bcc Mo v000	view	3252587
Relaxed energy as a function of tilt angle for a 111 symmetric tilt grain boundary in bcc Mo v000	view	1557446
Relaxed energy as a function of tilt angle for a 112 symmetric tilt grain boundary in bcc Mo v000	view	7184188

LatticeConstantCubicEnergy__TD_475411767977_005

Equilibrium lattice constant and cohesive energy of a cubic lattice at zero temperature and pressure v005

Creators: Junhao Li
Contributor: jl2922
Publication Year: 2018
DOI: https://doi.org/10.25950/f3eec5a9

Equilibrium lattice constant and cohesive energy of a cubic 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)
Equilibrium zero-temperature lattice constant for bcc Xe		view	1429

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 Mo v007	view	2207
Equilibrium zero-temperature lattice constant for bcc U v007	view	1919
Equilibrium zero-temperature lattice constant for diamond Mo v007	view	3583
Equilibrium zero-temperature lattice constant for diamond U v007	view	3359
Equilibrium zero-temperature lattice constant for diamond Xe v007	view	3679
Equilibrium zero-temperature lattice constant for fcc Mo v007	view	3647
Equilibrium zero-temperature lattice constant for fcc U v007	view	2175
Equilibrium zero-temperature lattice constant for fcc Xe v007	view	2367
Equilibrium zero-temperature lattice constant for sc Mo v007	view	2143
Equilibrium zero-temperature lattice constant for sc U v007	view	2207
Equilibrium zero-temperature lattice constant for sc Xe v007	view	2271

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 Mo v005		view	22604
Equilibrium lattice constants for hcp U v005		view	15027

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 bcc Mo at 293.15 K under a pressure of 0 MPa v002		view	534264

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 Xe v004		view	45584

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 Xe v002		view	67753

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 bcc Mo v004		view	15323
Broken-bond fit of high-symmetry surface energies in fcc Xe v004		view	17626

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 bcc Mo		view	370311

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 bcc Mo		view	540006

Errors

ElasticConstantsCubic__TD_011862047401_006

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

GrainBoundaryCubicCrystalSymmetricTiltRelaxedEnergyVsAngle__TD_410381120771_003

Test	Error Categories	Link to Error page
Relaxed energy as a function of tilt angle for a 100 symmetric tilt grain boundary in bcc Mo v001	other	view
Relaxed energy as a function of tilt angle for a 110 symmetric tilt grain boundary in bcc Mo v001	other	view
Relaxed energy as a function of tilt angle for a 111 symmetric tilt grain boundary in bcc Mo v001	other	view
Relaxed energy as a function of tilt angle for a 112 symmetric tilt grain boundary in bcc Mo v001	other	view

LatticeConstantCubicEnergy__TD_475411767977_006

Test	Error Categories	Link to Error page
Equilibrium zero-temperature lattice constant for bcc U	other	view
Equilibrium zero-temperature lattice constant for diamond Mo	other	view

LatticeConstantCubicEnergy__TD_475411767977_007

Test	Error Categories	Link to Error page
Equilibrium zero-temperature lattice constant for bcc Xe v007	other	view

LatticeConstantHexagonalEnergy__TD_942334626465_004

Test	Error Categories	Link to Error page
Equilibrium lattice constants for hcp Mo	other	view
Equilibrium lattice constants for hcp U	other	view

LatticeConstantHexagonalEnergy__TD_942334626465_005

Test	Error Categories	Link to Error page
Equilibrium lattice constants for hcp Xe v005	other	view

LinearThermalExpansionCoeffCubic__TD_522633393614_002

Test	Error Categories	Link to Error page
Linear thermal expansion coefficient of fcc Xe at 293.15 K under a pressure of 0 MPa v002	other	view

Files

CMakeLists.txt
LICENSE.CDDL
U_Mo_Xe.2013.eam.alloy
kimprovenance.edn
kimspec.edn

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EAM_Dynamo_SmirnovaKuskinStarikov_2013_UMoXe__MO_679329885632_005

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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Errors

Files

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