MEAM Potential for the Al-Ti system developed by Kim et al. (2016) v002

Young-Kwang Kim; Hong-Kyu Kim; Woo-Sang Jung; Byeong-Joo Lee

doi:10.25950/522a8fbb

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MEAM_LAMMPS_KimKimJung_2016_AlTi__MO_618133763375_002

Interatomic potential for Aluminum (Al), Titanium (Ti).
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Title A single sentence description.	MEAM Potential for the Al-Ti system developed by Kim et al. (2016) v002
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.	An interatomic potential for the Ti–Al binary system has been developed based on the second nearest-neighbor modified embedded-atom method (2NN MEAM) formalism. This potential describes fundamental materials properties (structural, thermodynamic, elastic, defect, deformation and thermal properties) of Ti–Al alloys in good agreements with experimental or first-principles data.
Species The supported atomic species.	Al, Ti
Disclaimer A statement of applicability provided by the contributor, informing users of the intended use of this KIM Item.	None
Content Origin	http://cmse.postech.ac.kr/home_2nnmeam

Contributor	Sang-Ho Oh
Maintainer	Sang-Ho Oh
Developer	Young-Kwang Kim Hong-Kyu Kim Woo-Sang Jung Byeong-Joo Lee
Published on KIM	2023
How to Cite	This Model originally published in [1] is archived in OpenKIM [2-5]. [1] Kim Y-K, Kim H-K, Jung W-S, Lee B-J. Atomistic modeling of the Ti–Al binary system. Computational materials science. 2016;119:1–8. doi:10.1016/j.commatsci.2016.03.038 — (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] Kim Y-K, Kim H-K, Jung W-S, Lee B-J. MEAM Potential for the Al-Ti system developed by Kim et al. (2016) v002. OpenKIM; 2023. doi:10.25950/522a8fbb [3] Afshar Y, Hütter S, Rudd RE, Stukowski A, Tipton WW, Trinkle DR, et al. The modified embedded atom method (MEAM) potential v002. OpenKIM; 2023. doi:10.25950/ee5eba52 [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. 42 Citations (35 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 . F. Zhao, J. Zhang, C. He, Y. Zhang, X. Gao, and L. Xie, “Molecular Dynamics Simulation on Creep Behavior of Nanocrystalline TiAl Alloy,” Nanomaterials. 2020. link Times cited: 10 Abstract: TiAl alloy represents a new class of light and heat-resistan… read more Abstract: TiAl alloy represents a new class of light and heat-resistant materials. In this study, the effect of temperature, pressure, and grain size on the high-temperature creep properties of nanocrystalline TiAl alloy have been studied through the molecular dynamics method. Based on this, the deformation mechanism of the different creep stages, including crystal structure, dislocation, and diffusion, has been explored. It is observed that the high-temperature creep performance of nanocrystalline TiAl alloy is significantly affected by temperature and stress. The higher is the temperature and stress, the greater the TiAl alloy’s steady-state creep rate and the faster the rapid creep stage. Smaller grain size accelerates the creep process due to the large volume fraction of the grain boundary. In the steady-state deformation stage, two kinds of creep mechanisms are manly noted, i.e., dislocation motion and grain boundary diffusion. At the same temperature, the creep mechanism is dominated by the dislocation motion in a high-stress field, and the creep mechanism is dominated by the diffusion creep in the low-stress field. However, it is observed to be mainly controlled by the grain boundary diffusion and lattice diffusion in the rapid creep stage. read less N. Walker, K.-M. Tam, B. R. Novak, and M. Jarrell, “Identifying structural changes with unsupervised machine learning methods,” Physical Review E. 2018. link Times cited: 8 Abstract: Unsupervised machine learning methods are used to identify s… read more Abstract: Unsupervised machine learning methods are used to identify structural changes using the melting point transition in classical molecular dynamics simulations as an example application of the approach. Dimensionality reduction and clustering methods are applied to instantaneous radial distributions of atomic configurations from classical molecular dynamics simulations of metallic systems over a large temperature range. Principal component analysis is used to dramatically reduce the dimensionality of the feature space across the samples using an orthogonal linear transformation that preserves the statistical variance of the data under the condition that the new feature space is linearly independent. From there, k-means clustering is used to partition the samples into solid and liquid phases through a criterion motivated by the geometry of the reduced feature space of the samples, allowing for an estimation of the melting point transition. This pattern criterion is conceptually similar to how humans interpret the data but with far greater throughput, as the shapes of the radial distributions are different for each phase and easily distinguishable by humans. The transition temperature estimates derived from this machine learning approach produce comparable results to other methods on similarly small system sizes. These results show that machine learning approaches can be applied to structural changes in physical systems. read less S. Sun, B. Ramachandran, and C. Wick, “Solid, liquid, and interfacial properties of TiAl alloys: parameterization of a new modified embedded atom method model,” Journal of Physics: Condensed Matter. 2018. link Times cited: 11 Abstract: New interatomic potentials for pure Ti and Al, and binary Ti… read more Abstract: New interatomic potentials for pure Ti and Al, and binary TiAl were developed utilizing the second nearest neighbour modified embedded-atom method (MEAM) formalism. The potentials were parameterized to reproduce multiple properties spanning bulk solids, solid surfaces, solid/liquid phase changes, and liquid interfacial properties. This was carried out using a newly developed optimization procedure that combined the simple minimization of a fitness function with a genetic algorithm to efficiently span the parameter space. The resulting MEAM potentials gave good agreement with experimental and DFT solid and liquid properties, and reproduced the melting points for Ti, Al, and TiAl. However, the surface tensions from the model consistently underestimated experimental values. Liquid TiAl’s surface was found to be mostly covered with Al atoms, showing that Al has a significant propensity for the liquid/air interface. read less A. Moradi, A. Heidari, K. Amini, F. Aghadavoudi, and R. Abedinzadeh, “Molecular modeling of Ti-6Al-4V alloy shot peening: the effects of diameter and velocity of shot particles and force field on mechanical properties and residual stress,” Modelling and Simulation in Materials Science and Engineering. 2021. link Times cited: 12 Abstract: Shot peening is a surface treatment process that is used for… read more Abstract: Shot peening is a surface treatment process that is used for the improvement of the mechanical properties of metallic alloys. The effects of sphere particle diameter and impact velocity on residual stress and mechanical properties of Ti-6Al-4V as titanium alloy are investigated in this study using molecular dynamics simulation. In this research, titanium atoms are simulated as a surface layer and the carbon steel atomic structure is modeled as the impacting particle in the shot peening process. Two types of molecular potential functions including embedded atom method (EAM) and Lennard-Jones (LJ) are used for molecular modeling of colliding atoms and the effect of these force fields on residual stress, hardness, surface roughness is investigated. Moreover, the effects of impacting particle velocity and diameter on these parameters are studied. The results show the amount of residual stress in the titanium surface layer increased by increasing the particle diameter and velocity of particles. The diameter and velocity of particles in the shot peening process has a significant effect on the mechanical behavior of the simulated titanium surface layer. The value of maximum compressive residual stress is −413 MPa which occurs in depth of 10 Å from the surface layer for 1 Å fs−1 velocity and 10 Å diameter of shot particle. Furthermore, the results show that the Vickers hardness of the titanium is increased by increasing the size and velocity of the carbon steel particle in both EAM and LJ potential functions. read less J. Raush, “Thermophysical and Thermochemical Property Measurement and Prediction of Liquid Metal Titanium Alloys with Applications in Additive Manufacturing.” 2016. link Times cited: 0 Abstract: ............................................................… read more Abstract: ........................................................................................................................................ xv Chapter 1 Motivation ...................................................................................................................... 1 1.1 Engineering Challenges.................................................................................................... 1 1.1.1 Additive Manufacturing ........................................................................................... 2 1.1.2 Powder Metallurgy.................................................................................................... 3 1.2 Materials Development Approach ................................................................................... 4 1.3 Focus of this Research...................................................................................................... 8 1.3.1 Objectives ............................................................................................................... 11 1.3.2 Project Timeline and Funding ................................................................................. 12 1.4 Outline of Dissertation ................................................................................................... 12 Chapter 2 Testing and Modeling Background .............................................................................. 14 2.1 Testing and Characterization of Liquid Metals .............................................................. 14 2.1.1 Traditional Thermophysical Property Measurement Techniques ........................... 14 2.1.2 Electrostatic Levitation ........................................................................................... 16 2.1.3 Additional Containerless Testing Techniques ........................................................ 31 2.2 Numerical Modeling ...................................................................................................... 31 2.2.1 CALPHAD .............................................................................................................. 32 2.2.2 Molecular Dynamics ............................................................................................... 33 Chapter 3 Test Method.................................................................................................................. 36 3. read less R. Xiao, K. L. Liu, Y. Ruan, and B. Wei, “Rapid acquisition of liquid thermophysical properties from pure metals to quaternary alloys by proposing a machine learning strategy,” Applied Physics Letters. 2023. link Times cited: 2 Abstract: The establishment of reliable materials genome databases inv… read more Abstract: The establishment of reliable materials genome databases involving the thermophysical properties of liquid metals and alloys promotes the progress of materials research and development, whereas acquiring these properties imposes great challenges on experimental investigation. Here, we proposed a deep learning method and achieved a deep neural network (DNN) interatomic potential for the entire Ti–Ni–Cr–Al system from pure metals to quaternary alloys. This DNN potential exhibited sufficient temperature and compositional transformability which extended beyond the training and provided the prediction of the liquid structure and thermophysical properties for metallic materials with both density functional theory accuracy and classic molecular dynamics efficiency. The predicted results agreed well with the reported experimental data. This work opens a feasible way to address the challenges of rapidly and accurately acquiring thermophysical properties data for liquid pure metals and multicomponent alloys, covering a broad temperature range from superheated to undercooled state. read less T. Li, C. Tian, A. Moridi, and J. Yeo, “Elucidating Interfacial Dynamics of Ti-Al Systems Using Molecular Dynamics Simulation and Markov State Modeling,” ACS applied materials & interfaces. 2023. link Times cited: 0 Abstract: Due to their remarkable mechanical and chemical properties, … read more Abstract: Due to their remarkable mechanical and chemical properties, Ti-Al-based materials are attracting considerable interest in numerous fields of engineering, such as automotive, aerospace, and defense. With their low density, high strength, and resistance to corrosion and oxidation, these intermetallic alloys and metal-compound composites have found diverse applications. However, additive manufacturing and heat treatment of Ti-Al alloys frequently lead to brittleness and severe formation of defects. The present study delves into the interfacial dynamics of these Ti-Al systems, particularly focusing on the behavior of Ti and Al atoms in the presence of TiAl3 grain boundaries under experimental heat treatment conditions. Using a combination of molecular dynamics and Markov state modeling, we scrutinize the kinetic processes involved in the formation of TiAl3. The molecular dynamics simulation indicates that at the early stage of heat treatment, the predominating process is the diffusion of Al atoms toward the Ti surface through the TiAl3 grain boundaries. Markov state modeling identifies three distinct dynamic states of Al atoms within the Ti/Al mixture that forms during the process, each exhibiting a unique spatial distribution. Using transition time scales as a qualitative measure of the rapidness of the dynamics, it is observed that the Al dynamics is significantly less rapid near the Ti surface compared to the Al surface. Put together, the results offer a comprehensive understanding of the interfacial dynamics and reveal a three-stage diffusion mechanism. The process initiates with the premelting of Al, proceeds with the prevalent diffusion of Al atoms toward the Ti surface, and eventually ceases as the Ti concentration within the mixture progressively increases. The insights gained from this study could contribute significantly to the control and optimization of manufacturing processes for these high-performing Ti-Al-based materials. read less J. Lu, J. Wang, K. Wan, Y. Chen, H. Wang, and X. Shi, “An accurate interatomic potential for the TiAlNb ternary alloy developed by deep neural network learning method.,” The Journal of chemical physics. 2023. link Times cited: 0 Abstract: The complex phase diagram and bonding nature of the TiAl sys… read more Abstract: The complex phase diagram and bonding nature of the TiAl system make it difficult to accurately describe its various properties and phases by traditional atomistic force fields. Here, we develop a machine learning interatomic potential with a deep neural network method for the TiAlNb ternary alloy based on a dataset built by first-principles calculations. The training set includes bulk elementary metals and intermetallic structures with slab and amorphous configurations. This potential is validated by comparing bulk properties-including lattice constant and elastic constants, surface energies, vacancy formation energies, and stacking fault energies-with their respective density functional theory values. Moreover, our potential could accurately predict the average formation energy and stacking fault energy of γ-TiAl doped with Nb. The tensile properties of γ-TiAl are simulated by our potential and verified by experiments. These results support the applicability of our potential under more practical conditions. read less J.-yao Liu and L. Zhang, “Investigation on the Diffusion Behaviors and Mechanical Properties of the Ti/Al Interface Using Molecular Dynamics Simulation,” Journal of Materials Engineering and Performance. 2023. link Times cited: 0 G. Poletaev, Y. Bebikhov, A. S. Semenov, and A. Sitnikov, “Molecular Dynamics Investigation of the Effect of the Interface Orientation on the Intensity of Titanium Dissolution in Crystalline and Amorphous Aluminum,” Journal of Experimental and Theoretical Physics. 2023. link Times cited: 0 K. A. Nair and S. Ghosh, “Crack tip enhanced phase-field model for crack evolution in crystalline Ti6Al from concurrent crystal plasticity FE-molecular dynamics simulations,” European Journal of Mechanics - A/Solids. 2023. link Times cited: 1 B. Zhai and H. P. Wang, “Accurate interatomic potential for the nucleation in liquid Ti-Al binary alloy developed by deep neural network learning method,” Computational Materials Science. 2023. link Times cited: 2 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 N. Dhariwal, A. S. M. Miraz, W. Meng, and C. Wick, “Strengthening the Ti/Tin Interface Against Shear Failure with Al Dopants:A Molecular Dynamics Study,” SSRN Electronic Journal. 2022. link Times cited: 1 R. Fereidonnejad, A. O. Moghaddam, and M. Moaddeli, “Modified embedded-atom method interatomic potentials for Al-Ti, Al-Ta, Al-Zr, Al-Nb and Al-Hf binary intermetallic systems,” Computational Materials Science. 2022. link Times cited: 5 B. Zhai, Q. Wang, J. Chang, and H. Wang, “Primary Phase Selection Related to Liquid Local Structure Within Ti–Al–V Alloy Solidified During Free Fall,” Metallurgical and Materials Transactions B. 2022. link Times cited: 1 G. Poletaev, Y. Bebikhov, and A. Semenov, “The Effect of Ti–Al Interfacial Orientation and Ti Deformation on the Interdiffusion Rate at Temperatures above the Melting Point of Al: A Molecular-Dynamics Study,” Technical Physics Letters. 2022. link Times cited: 0 G. Poletaev, A. Sitnikov, V. Yakovlev, and V. Filimonov, “Melting Point of Ti, Ti3Al, TiAl, and TiAl3 Nanoparticles Versus Their Diameter in Vacuum and Liquid Aluminum: Molecular Dynamics Investigation,” Journal of Experimental and Theoretical Physics. 2022. link Times cited: 1 S. Attarian and S. Xiao, “Development of a 2NN-MEAM potential for Ti B system and studies of the temperature dependence of the nanohardness of TiB2,” Computational Materials Science. 2022. link Times cited: 3 H. Xiang and W. Guo, “A newly developed interatomic potential of Nb−Al−Ti ternary systems for high-temperature applications,” Acta Mechanica Sinica. 2022. link Times cited: 5 G. Poletaev, Y. Bebikhov, A. Semenov, and M. Starostenkov, “Self-diffusion in melts of Ni-Al and Ti-Al systems: molecular dynamics study,” Letters on Materials. 2021. link Times cited: 3 Abstract: Self-diffusion in liquid alloys of Ni-Al and Ti-Al systems, … read more Abstract: Self-diffusion in liquid alloys of Ni-Al and Ti-Al systems, depending on the concentration of the components, as well as in pure metals Al, Ni, and Ti, has been studied using the molecular dynamics method. Knowledge of the diffusion parameters in melts of binary systems, such as Ni-Al and Ti-Al, is necessary for a more detailed understanding and prediction of the processes occurring during combustion synthesis and in the manufacture of wares by melt casting. For the considered systems, self-diffusion characteristics (activation energy and preexponential factor in the corresponding Arrhenius equation) are found separately for atoms of different types. Good agreement of diffusion characteristics with experimental data was obtained for pure metal melts, which indicates the physical realism of the molecular dynamics model and EAM potentials used. In addition to pure metals, three component ratios were considered for each system: A 75 B 25 , A 50 B 50 and A 25 B 75 . According to the data obtained, the activation energy of diffusion substantially depends on the concentration of the components, and the highest, among the considered compositions, for Ni 75 Al 25 for Ni-Al system and for Ti 50 Al 50 for Ti-Al system. At the same time, no significant predominance of diffusion mobility of atoms of different types was observed for all the mixture compositions considered. read less J.-yao Liu and L. Zhang, “Molecular Dynamics Simulation of the Tensile Deformation Behavior of the γ(TiAl)/α2(Ti3Al) Interface at Different Temperatures,” Journal of Materials Engineering and Performance. 2021. link Times cited: 3 G. Poletaev, “Self-Diffusion in Liquid and Solid Alloys of the Ti–Al System: Molecular Dynamics Simulation,” Journal of Experimental and Theoretical Physics. 2021. link Times cited: 6 Y. Yang, M. Liu, S. Zhou, W. Ren, Q. Zhou, and S. Lan, “Breaking through the strength-ductility trade-off in graphene reinforced Ti6Al4V composites,” Journal of Alloys and Compounds. 2021. link Times cited: 22 G. Poletaev, A. Sitnikov, and V. Filimonov, “Melting temperature of Ti and TiAl nanoparticles in vacuum and in Al matrix depending on their diameter: molecular dynamics study,” Letters on Materials. 2021. link Times cited: 2 Abstract: The dependence of the melting temperature of Ti and TiAl nan… read more Abstract: The dependence of the melting temperature of Ti and TiAl nanoparticles on their diameter in vacuum and in Al matrix was studied by the method of molecular dynamics using EAM potentials of Zope and Mishin. Particles with a diameter of 2.5 to 12 nm were considered. The obtained values of the melting point are in good agreement with the approximation curves constructed on the basis that the decrease in the melting temperature is proportional to the ratio of the surface area of the particle to its volume. Wherein the values of the melting temperature of Ti and TiAl particles in the aluminum matrix turned out to be lower than those of particles in vacuum, which is explained by the smearing and disordering of the interface due to mutual diffusion. As the size of particles in vacuum and in aluminum increased, the values of their melting points tended to the same value, which is explained by the decrease in the role of the diffusion-blurred interface with an increase in the particle diameter. The particles began to melt from the surface. The velocity of movement of the melting front depended on temperature and increased with increasing temperature. In the case of particles in aluminum matrix, at temperatures close to the particle melting point, mutual diffusion was significantly accelerated due to melting of the particle boundary layer. Al atoms penetrating into the particle accelerated the movement of the melting front, rapidly occupying the next destroyed layer of the particle. read less Q. Pei, M. Jhon, S. Quek, and Z. Wu, “A systematic study of interatomic potentials for mechanical behaviours of Ti-Al alloys,” Computational Materials Science. 2021. link Times cited: 19 S. Wang, F. Xie, X.-qing Wu, T. Lv, and Y. Ma, “Microstructure and high temperature oxidation behavior of the Al2O3 CPED coating on TiAl alloy,” Journal of Alloys and Compounds. 2020. link Times cited: 24 R. Voskoboinikov, “An insight into radiation resistance of D019 Ti3Al intermetallics,” Journal of Nuclear Materials. 2019. link Times cited: 15 M. Elkhateeb and Y. Shin, “Molecular dynamics-based cohesive zone representation of Ti6Al4V/TiC composite interface,” Materials & Design. 2018. link Times cited: 43 P. Liu, X. Han, D. Sun, and Q. Wang, “Development and application of a ternary Ti-Al-N interatomic potential for Ti2AlN/TiAl composite,” Journal of Alloys and Compounds. 2018. link Times cited: 20 Y.-K. Kim, H. Kim, W. Jung, and B.-J. Lee, “Development and application of Ni-Ti and Ni-Al-Ti 2NN-MEAM interatomic potentials for Ni-base superalloys,” Computational Materials Science. 2017. link Times cited: 24 C.-jun Wu, B.-J. Lee, and X. Su, “Modified embedded-atom interatomic potential for Fe-Ni, Cr-Ni and Fe-Cr-Ni systems,” Calphad-computer Coupling of Phase Diagrams and Thermochemistry. 2017. link Times cited: 60 S. Y. Korostelev, E. E. Slyadnikov, and I. Turchanovsky, “The resistance of amorphous metals to thermal effects. Molecular dynamics modeling,” PHYSICAL MESOMECHANICS OF CONDENSED MATTER: Physical Principles of Multiscale Structure Formation and the Mechanisms of Nonlinear Behavior: MESO2022. 2023. link Times cited: 0 A. I. Dmitriev, A. Nikonov, and A. R. Shugurov, “Molecular dynamics study of the strength properties of TixAl1-xN coatings,” PHYSICAL MESOMECHANICS OF CONDENSED MATTER: Physical Principles of Multiscale Structure Formation and the Mechanisms of Nonlinear Behavior: MESO2022. 2023. link Times cited: 0 Y. Jian, Z. Huang, J. Xing, L. Sun, Y. Liu, and P. Gao, “Phase stability, mechanical properties and electronic structures of Ti Al binary compounds by first principles calculations,” Materials Chemistry and Physics. 2019. link Times cited: 47 H. Ganesan, I. Scheider, and C. Cyron, “Quantifying the High-Temperature Separation Behavior of Lamellar Interfaces in γ-Titanium Aluminide Under Tensile Loading by Molecular Dynamics,” Frontiers in Materials. 2021. link Times cited: 4 Abstract: γ-titanium aluminide (TiAl) alloys with fully lamellar micro… read more Abstract: γ-titanium aluminide (TiAl) alloys with fully lamellar microstructure possess excellent properties for high-temperature applications. Such fully lamellar microstructure has interfaces at different length scales. The separation behavior of the lamellae at these interfaces is crucial for the mechanical properties of the whole material. Unfortunately, quantifying it by experiments is difficult. Therefore, we use molecular dynamics (MD) simulations to this end. Specifically, we study the high-temperature separation behavior under tensile loading of the four different kinds of lamellar interfaces appearing in TiAl, namely, the γ / α 2 , γ / γ PT , γ / γ TT , and γ / γ RB interfaces. In our simulations, we use two different atomistic interface models, a defect-free (Type-1) model and a model with preexisting voids (Type-2). Clearly, the latter is more physical but studying the former also helps to understand the role of defects. Our simulation results show that among the four interfaces studied, the γ / α 2 interface possesses the highest yield strength, followed by the γ / γ PT , γ / γ TT , and γ / γ RB interfaces. For Type-1 models, our simulations reveal failure at the interface for all γ/γ interfaces but not for the γ / α 2 interface. By contrast, for Type-2 models, we observe for all the four interfaces failure at the interface. Our atomistic simulations provide important data to define the parameters of traction–separation laws and cohesive zone models, which can be used in the framework of continuum mechanical modeling of TiAl. Temperature-dependent model parameters were identified, and the complete traction–separation behavior was established, in which interface elasticity, interface plasticity, and interface damage could be distinguished. By carefully eliminating the contribution of bulk deformation from the interface behavior, we were able to quantify the contribution of interface plasticity and interface damage, which can also be related to the dislocation evolution and void nucleation in the atomistic simulations. read less Z. Yin et al., “Phase stability, brittle-ductile transition, and electronic structures of the TiAl alloying with Fe, Ru, Ge, and Sn: a first-principle investigation,” Journal of Molecular Modeling. 2020. link Times cited: 5 J. Kundu, A. Chakraborty, and S. Kundu, “Bonding pressure effects on characteristics of microstructure, mechanical properties, and mass diffusivity of Ti-6Al-4V and TiAlNb diffusion-bonded joints,” Welding in the World. 2020. link Times cited: 3 N. Pushilina, V. Kudiiarov, M. Syrtanov, and E. Kashkarov, “Effect of the Beam Current during the Electron-Beam Melting of Titanium Alloy Ti–6Al–4V on the Structural Features and Phase Transitions in Gas-Phase Hydrogenation,” Journal of Surface Investigation: X-ray, Synchrotron and Neutron Techniques. 2019. link Times cited: 3 D. Dickel, C. Barrett, R. Cariño, M. Baskes, and M. Horstemeyer, “Mechanical instabilities in the modeling of phase transitions of titanium,” Modelling and Simulation in Materials Science and Engineering. 2018. link Times cited: 16 Abstract: In this paper, we demonstrate that previously observed β to … read more Abstract: In this paper, we demonstrate that previously observed β to α transitions for titanium interatomic potentials available in the literature arose from a mechanical instability and thus the potentials underestimated the correct thermodynamic phase transition temperature by hundreds of degrees. Using a relative free energy method for the two phases to calculate the true transition temperature, we present a new modified embedded atom method potential for titanium that shows a transition temperature of 1155 ± 2 K in excellent agreement with the experimentally observed transition. This free energy approach avoids the problems of irreversibility which occur when one relies on direct observation of the phase transition in molecular dynamics simulation. Other transformation mechanisms in addition to the mechanical instability are also considered. Finally, the new potential predicts the proper c-axis plastic twinning for titanium under compression making it the only potential that correctly predicts the phase transition temperature and the plastic behavior of α Ti. read less N. Pushilina et al., “Hydrogen-Induced Phase Transformation and Microstructure Evolution for Ti-6Al-4V Parts Produced by Electron Beam Melting.” 2018. link Times cited: 37 Abstract: In this paper, phase transitions and microstructure evolutio… read more Abstract: In this paper, phase transitions and microstructure evolution in titanium Ti-6Al-4V alloy parts produced by electron beam melting (EBM) under hydrogenation was investigated. Hydrogenation was carried out at the temperature of 650 °C to the absolute hydrogen concentrations in the samples of 0.29, 0.58, and 0.90 wt. %. Comparative analysis of microstructure changes in Ti-6Al-4V alloy parts was performed using scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray diffraction (XRD). Furthermore, in-situ XRD was used to investigate the phase transitions in the samples during hydrogenation. The structure of Ti-6Al-4V parts produced by EBM is represented by the α phase plates with the transverse length of 0.2 μm, the β phase both in the form of plates and globular grains, and metastable α″ and ω phases. Hydrogenation to the concentration of 0.29 wt. % leads to the formation of intermetallic Ti3Al phase. The dimensions of intermetallic Ti3Al plates and their volume fraction increase significantly with hydrogen concentration up to 0.58 wt. % along with precipitation of nano-sized crystals of titanium δ hydrides. Individual Ti3Al plates decay into nanocrystals with increasing hydrogen concentration up to 0.9 wt. % accompanied by the increase of proportion and size of hydride plates. Hardness of EBM Ti-6Al-4V alloy decreases with hydrogen content. read less M. Wang, W.-xian Wang, H. Chen, and Y. Li, “Understanding micro-diffusion bonding from the fabrication of B4C/Ni composites,” International Journal of Minerals, Metallurgy, and Materials. 2018. link Times cited: 7
Funding	Not available

Short KIM ID The unique KIM identifier code.	MO_618133763375_002
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.	MEAM_LAMMPS_KimKimJung_2016_AlTi__MO_618133763375_002
DOI	10.25950/522a8fbb https://doi.org/10.25950/522a8fbb https://commons.datacite.org/doi.org/10.25950/522a8fbb
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 MEAM_LAMMPS__MD_249792265679_002
Driver	MEAM_LAMMPS__MD_249792265679_002
KIM API Version	2.2
Potential Type	meam

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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 3.50912e-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
N/A Unable to perform verification check due to an error.	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: Ti

Species: Al

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: Ti

Species: Al

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: Al

Species: Ti

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: Ti

Species: Al

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: Al

Species: Ti

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: Al

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: Al

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: Al

Species: Ti

Cubic Crystal Basic Properties Table

Species: Al

Species: Ti

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 Al v004	view	6258
Cohesive energy versus lattice constant curve for bcc Ti v004	view	6184
Cohesive energy versus lattice constant curve for diamond Al v004	view	5928
Cohesive energy versus lattice constant curve for diamond Ti v004	view	6405
Cohesive energy versus lattice constant curve for fcc Al v004	view	5610
Cohesive energy versus lattice constant curve for fcc Ti v004	view	6047
Cohesive energy versus lattice constant curve for sc Al v004	view	6296
Cohesive energy versus lattice constant curve for sc Ti v004	view	5890

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 Al at zero temperature v006	view	18042
Elastic constants for bcc Ti at zero temperature v006	view	18111
Elastic constants for diamond Al at zero temperature v001	view	48516
Elastic constants for fcc Al at zero temperature v006	view	17843
Elastic constants for fcc Ti at zero temperature v006	view	34493
Elastic constants for sc Al at zero temperature v006	view	19216
Elastic constants for sc Ti at zero temperature v006	view	19554

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 AlTi in AFLOW crystal prototype A2B_oC12_65_acg_h v002	view	84664
Equilibrium crystal structure and energy for AlTi in AFLOW crystal prototype A2B_tI24_141_2e_e v002	view	48669
Equilibrium crystal structure and energy for AlTi in AFLOW crystal prototype A3B_cP4_221_c_a v002	view	81175
Equilibrium crystal structure and energy for AlTi in AFLOW crystal prototype A3B_tI8_139_ad_b v002	view	53469
Equilibrium crystal structure and energy for AlTi in AFLOW crystal prototype AB2_cF12_216_a_bc v002	view	117204
Equilibrium crystal structure and energy for AlTi in AFLOW crystal prototype AB3_cF16_225_a_bc v002	view	117057
Equilibrium crystal structure and energy for AlTi in AFLOW crystal prototype AB3_hP8_194_c_h v002	view	59485
Equilibrium crystal structure and energy for AlTi in AFLOW crystal prototype AB_tP2_123_a_d v002	view	46907

EquilibriumCrystalStructure__TD_457028483760_003

Equilibrium structure and energy for a crystal structure at zero temperature and pressure v003

Creators:
Contributor: ilia
Publication Year: 2025
DOI: https://doi.org/10.25950/866c7cfa

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 Al in AFLOW crystal prototype A_cF4_225_a v003	view	146189
Equilibrium crystal structure and energy for Ti in AFLOW crystal prototype A_cF4_225_a v003	view	177756
Equilibrium crystal structure and energy for Al in AFLOW crystal prototype A_cI2_229_a v003	view	198814
Equilibrium crystal structure and energy for Ti in AFLOW crystal prototype A_cI2_229_a v003	view	149044
Equilibrium crystal structure and energy for Ti in AFLOW crystal prototype A_hP2_194_c v003	view	146432
Equilibrium crystal structure and energy for Ti in AFLOW crystal prototype A_hP3_191_ad v003	view	174393

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	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)
Relaxed energy as a function of tilt angle for a 100 symmetric tilt grain boundary in fcc Al v003		view	22397010
Relaxed energy as a function of tilt angle for a 110 symmetric tilt grain boundary in fcc Al v001		view	69186872

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 Al v007	view	14133
Equilibrium zero-temperature lattice constant for bcc Ti v007	view	11779
Equilibrium zero-temperature lattice constant for diamond Al v007	view	13546
Equilibrium zero-temperature lattice constant for diamond Ti v007	view	14829
Equilibrium zero-temperature lattice constant for fcc Al v007	view	12663
Equilibrium zero-temperature lattice constant for fcc Ti v007	view	13914
Equilibrium zero-temperature lattice constant for sc Al v007	view	11558
Equilibrium zero-temperature lattice constant for sc Ti v007	view	12957

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 Al at 293.15 K under a pressure of 0 MPa v002		view	3411674

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 Al v004		view	113523

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 Al v002		view	42527250

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 Al v004		view	136718

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 Al		view	560252

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 Al		view	887569

Errors

ElasticConstantsCubic__TD_011862047401_006

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

EquilibriumCrystalStructure__TD_457028483760_000

Test	Error Categories	Link to Error page
Equilibrium crystal structure and energy for AlTi in AFLOW crystal prototype AB_tP2_123_a_d v000	other	view

GrainBoundaryCubicCrystalSymmetricTiltRelaxedEnergyVsAngle__TD_410381120771_003

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

LatticeConstantHexagonalEnergy__TD_942334626465_005

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

LinearThermalExpansionCoeffCubic__TD_522633393614_001

Test	Error Categories	Link to Error page
Linear thermal expansion coefficient of fcc Al at 293.15 K under a pressure of 0 MPa v001	other	view

PhononDispersionCurve__TD_530195868545_004

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

SurfaceEnergyCubicCrystalBrokenBondFit__TD_955413365818_004

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

No Driver

Verification Check	Error Categories	Link to Error page
DimerContinuityC1__VC_303890932454_005	other	view

Files

AlTi.meam
CMakeLists.txt
LICENSE
elems.meam
kimprovenance.edn
kimspec.edn
library.meam

Download

MEAM_LAMMPS_KimKimJung_2016_AlTi__MO_618133763375_002.txz	Tar+XZ	Linux and OS X archive
MEAM_LAMMPS_KimKimJung_2016_AlTi__MO_618133763375_002.zip	Zip	Windows archive

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This Model requires a Model Driver. Archives for the Model Driver MEAM_LAMMPS__MD_249792265679_002 appear below.

MEAM_LAMMPS__MD_249792265679_002.txz	Tar+XZ	Linux and OS X archive
MEAM_LAMMPS__MD_249792265679_002.zip	Zip	Windows archive

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MEAM_LAMMPS_KimKimJung_2016_AlTi__MO_618133763375_002

Verification Check Dashboard

Visualizers (in-page)

BCC Lattice Constant

Cohesive Energy Graph

Diamond Lattice Constant

Dislocation Core Energies

FCC Elastic Constants

FCC Lattice Constant

FCC Stacking Fault Energies

FCC Surface Energies

SC Lattice Constant

Cubic Crystal Basic Properties Table

Tests

Errors

Files

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