LAMMPS MEAM potential for Fe developed by Asadi et al. (2015) v001

Ebrahim Asadi; Mohsen Asle Zaeem; Sasan Nouranian; Michael I. Baskes

doi:10.25950/fa2eccc9

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Sim_LAMMPS_MEAM_AsadiZaeemNouranian_2015_Fe__SM_042630680993_001

Interatomic potential for Iron (Fe).
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Title A single sentence description.	LAMMPS MEAM potential for Fe developed by Asadi et al. (2015) v001
Description	In this paper, molecular dynamics (MD) simulations based on the modified-embedded atom method (MEAM) and a phase-field crystal (PFC) model are utilized to quantitatively investigate the solid-liquid properties of Fe. A set of second nearest-neighbor MEAM parameters for high-temperature applications are developed for Fe, and the solid-liquid coexisting approach is utilized in MD simulations to accurately calculate the melting point, expansion in melting, latent heat, and solid-liquid interface free energy, and surface anisotropy. The required input properties to determine the PFC model parameters, such as liquid structure factor and fluctuations of atoms in the solid, are also calculated from MD simulations. The PFC parameters are calculated utilizing an iterative procedure from the inputs of MD simulations. The solid-liquid interface free energy and surface anisotropy are calculated using the PFC simulations. Very good agreement is observed between the results of our calculations from MEAM-MD and PFC simulations and the available modeling and experimental results in the literature. As an application of the developed model, the grain boundary free energy of Fe is calculated using the PFC model and the results are compared against experiments. HISTORY: Changes in version 001: * Change ibar parameter in library file to be integer rather than float to avoid LAMMPS type check error
Species The supported atomic species.	Fe
Disclaimer A statement of applicability provided by the contributor, informing users of the intended use of this KIM Item.	None
Content Origin	NIST IPRP (https://www.ctcms.nist.gov/potentials/Fe.html)

Contributor	Daniel S. Karls
Maintainer	Daniel S. Karls
Developer	Ebrahim Asadi Mohsen Asle Zaeem Sasan Nouranian Michael I. Baskes
Published on KIM	2021
How to Cite	This Simulator Model originally published in [1] is archived in OpenKIM [2-4]. [1] Asadi E, Asle Zaeem M, Nouranian S, Baskes MI. Quantitative modeling of the equilibration of two-phase solid-liquid Fe by atomistic simulations on diffusive time scales. Phys Rev B. 2015;91:024105. doi:10.1103/PhysRevB.91.024105 — (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] Asadi E, Zaeem MA, Nouranian S, Baskes MI. LAMMPS MEAM potential for Fe developed by Asadi et al. (2015) v001. OpenKIM; 2021. doi:10.25950/fa2eccc9 [3] 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 [4] 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. 59 Citations (42 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 . 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 Y. Shiihara et al., “Artificial Neural Network Molecular Mechanics of Iron Grain Boundaries,” EngRN: Metals & Alloys (Topic). 2021. link Times cited: 9 Abstract: This study reports grain boundary (GB) energy calculations f… read more Abstract: This study reports grain boundary (GB) energy calculations for 46 symmetric-tilt GBs in α-iron using molecular mechanics based on an artificial neural network (ANN) potential and compares the results with calculations based on the density functional theory (DFT), the embedded atom method (EAM), and the modified EAM (MEAM). The results by the ANN potential are in excellent agreement with those of the DFT (5 % on average), while the EAM and MEAM significantly differ from the DFT results (about 27 % on average). In a uniaxial tensile calculation of Σ3(1‾12) GB, the ANN potential reproduced the brittle fracture tendency of the GB observed in the DFT while the EAM and MEAM showed mistakenly showed ductile behaviors. These results demonstrate the effectiveness of the ANN potential in grain boundary calculations of iron as a fast and accurate simulation highly in demand in the modern industrial world. read less M. Fellinger, A. M. Tan, L. Hector, and D. Trinkle, “Geometries of edge and mixed dislocations in bcc Fe from first-principles calculations,” Physical Review Materials. 2018. link Times cited: 21 Abstract: We use DFT to compute core structures of $a_0[100](010)$ edg… read more Abstract: We use DFT to compute core structures of $a_0[100](010)$ edge, $a_0[100](011)$ edge, $a_0/2[\bar{1}\bar{1}1](1\bar{1}0)$ edge, and $a_0/2[111](1\bar{1}0)$ $71^{\circ}$ mixed dislocations in bcc Fe. The calculations use flexible boundary conditions (FBC), which allow dislocations to relax as isolated defects by coupling the core to an infinite harmonic lattice through the lattice Green function (LGF). We use LGFs of dislocated geometries in contrast to previous FBC-based dislocation calculations that use the bulk crystal LGF. Dislocation LGFs account for changes in topology in the core as well as strain throughout the lattice. A bulk-like approximation for the force constants in a dislocated geometry leads to LGFs that optimize the cores of the $a_0[100](010)$ edge, $a_0[100](011)$ edge, and $a_0/2[111](1\bar{1}0)$ $71^{\circ}$ mixed dislocations. This approximation fails for the $a_0/2[\bar{1}\bar{1}1](1\bar{1}0)$ dislocation, so here we derive the LGF using accurate force constants from a Gaussian approximation potential. The standard deviations of dislocation Nye tensor distributions quantify the widths of the cores. The relaxed cores are compact, and the magnetic moments on the Fe atoms closely follow the volumetric strain distributions in the cores. We also compute the core structures of these dislocations using eight different classical interatomic potentials, and quantify symmetry differences between the cores using the Fourier coefficients of their Nye tensor distributions. Most of the core structures computed using the classical potentials agree well with DFT results. The DFT geometries provide benchmarking for classical potential studies of work-hardening, as well as substitutional and interstitial sites for computing solute-dislocation interactions that serve as inputs for mesoscale models of solute strengthening and solute diffusion near dislocations. read less E. Asadi and M. A. Zaeem, “The anisotropy of hexagonal close-packed and liquid interface free energy using molecular dynamics simulations based on modified embedded-atom method,” Acta Materialia. 2016. link Times cited: 37 S. Kavousi, “Combined Molecular Dynamics and Phase Field Simulation of Crystal Melt Interfacial Properties and Microstructure Evolution during Rapid Solidification of TI-NI Alloys.” 2019. link Times cited: 0 O. Klimanova, T. Miryashkin, and A. Shapeev, “Accurate melting point prediction through autonomous physics-informed learning,” Physical Review B. 2023. link Times cited: 0 Abstract: We present an algorithm for computing melting points by auto… read more Abstract: We present an algorithm for computing melting points by autonomously learning from coexistence simulations in the NPT ensemble. Given the interatomic interaction model, the method makes decisions regarding the number of atoms and temperature at which to conduct simulations, and based on the collected data predicts the melting point along with the uncertainty, which can be systematically improved with more data. We demonstrate how incorporating physical models of the solid-liquid coexistence evolution enhances the algorithm's accuracy and enables optimal decision-making to effectively reduce predictive uncertainty. To validate our approach, we compare the results of 20 melting point calculations from the literature to the results of our calculations, all conducted with same interatomic potentials. Remarkably, we observe significant deviations in about one-third of the cases, underscoring the need for accurate and reliable algorithms for materials property calculations. read less S. Kavousi, V. Ankudinov, P. Galenko, and M. A. Zaeem, “Atomistic-informed kinetic phase-field modeling of non-equilibrium crystal growth during rapid solidification,” Acta Materialia. 2023. link Times cited: 1 C. Zhang, Q. Liao, X. Zhang, F. Ma, M. Wu, and Q. Xu, “Characterization of porosity in lack of fusion pores in selective laser melting using the wavefunction,” Materials Research Express. 2022. link Times cited: 0 Abstract: Selective laser melting (SLM) is used extensively in the man… read more Abstract: Selective laser melting (SLM) is used extensively in the manufacture of components for both production and domestic applications. However, the lack of fusion pores is one of the most common defects in the SLM process, affecting the performance and life of the part and hindering the development of the SLM process. Meanwhile, the defects are particularly sensitive to changes in SLM process parameters. The micro-selective laser melting (μ SLM) model was established by molecular dynamics simulation, and the lack of fusion pores in the melt pool was analyzed by a multifunctional wavefunction analyzer to understand the difference of the porosities under different processes. The results show that both laser power and scanning speed can prolong the existence time of the melt pool by changing the input energy density. The melted powder has more time to fill the lack of fusion pores, thus reducing the porosity. The larger scanning spacing hinders the combination of adjacent melt pools, leading to an increase in porosity. Reducing scanning spacing will lead to sintering or remelting, thus improving the bonding quality of adjacent melt pools and effectively reducing porosity. read less Y. Lei et al., “An Embedded-Atom Method Potential for studying the properties of Fe-Pb solid-liquid interface,” Journal of Nuclear Materials. 2022. link Times cited: 1 L. Zhang, G. Csányi, E. Giessen, and F. Maresca, “Atomistic fracture in bcc iron revealed by active learning of Gaussian approximation potential,” npj Computational Materials. 2022. link Times cited: 3 G. Azizi, S. Kavousi, and M. A. Zaeem, “Interactive Effects of Interfacial Energy Anisotropy and Solute Transport on Solidification Patterns of Al-Cu Alloys,” Acta Materialia. 2022. link Times cited: 16 S. A. Etesami, M. Laradji, and E. Asadi, “The influence of Pb content on the interfacial free energy of solid Sn in eutectic Pb–Sn liquid mixtures using molecular dynamics simulations,” Molecular Simulation. 2022. link Times cited: 2 Abstract: ABSTRACT The solid–liquid interfacial free energy (γ) for bi… read more Abstract: ABSTRACT The solid–liquid interfacial free energy (γ) for binary Pb–Sn systems at different concentrations and crystal orientations is calculated using molecular dynamics (MD) simulation that employs a modified-embedded atom method interatomic potential. First, the solid–liquid interface is constructed in MD simulations according to its calculated phase diagram. Then, the capillary fluctuation method (CFM) is employed for the calculation of the solid–liquid interfacial stiffness. A novel systematic approach is proposed that eliminates the error in the linear approximation of the CFM. The results show that orientation of the solid Sn crystal does not have a significant influence on the value of the stiffness, which explains the experimentally observed spherical shape of Sn particles in eutectic liquid Pb–Sn mixtures. The calculated γ for pure Sn is in good agreement with experimental counterparts. In addition, this study finds that γ slightly increases with increasing the concentration of Pb in the eutectic liquid Pb–Sn mixture, which agrees with available experimental data. read less A. Mahata, T. Mukhopadhyay, and M. A. Zaeem, “Modified embedded-atom method interatomic potentials for Al-Cu, Al-Fe and Al-Ni binary alloys: From room temperature to melting point,” Computational Materials Science. 2022. link Times cited: 27 S. Kavousi, A. Gates, L. Jin, and M. A. Zaeem, “A Temperature-Dependent Atomistic-Informed Phase-Field Model to Study Dendritic Growth,” Journal of Crystal Growth. 2021. link Times cited: 6 L. Gui et al., “Effects of Carbon Segregation and Interface Roughness on the Mobility of Solid-liquid Interface in Fe-C Alloy: A Molecular Dynamics Study,” Materialia. 2021. link Times cited: 2 M. Khalid, J. Friis, P. H. Ninive, K. Marthinsen, I. G. Ringdalen, and A. Strandlie, “Modified embedded atom method potential for Fe-Al intermetallics mechanical strength: A comparative analysis of atomistic simulations,” Physica B-condensed Matter. 2021. link Times cited: 4 Z. Chen, Y. Hu, X. He, T. Xiao, L. Hao, and Y. Ruan, “Phase-field crystal method for multiscale microstructures with cubic term,” Materials Today Communications. 2021. link Times cited: 0 Z. Wu, R. Wang, L. Zhu, S. Pattamatta, and D. Srolov, “Revealing and Controlling the Core of Screw Dislocations in BCC Metals.” 2021. link Times cited: 0 Abstract: Body-centred-cubic (BCC) transition metals (TMs) tend to b… read more Abstract: Body-centred-cubic (BCC) transition metals (TMs) tend to be brittle at low temperatures, posing significant challenges in their processing and major concerns for damage tolerance in critical load-carrying applications. The brittleness is largely dictated by the screw dislocation core structure; the nature and control of which has remained a puzzle for nearly a century. Here, we introduce a universal model and a physics-based material index χ that guides the manipulation of dislocation core structure in all pure BCC metals and alloys. We show that the core structure, commonly classified as degenerate (D) or non-degenerate (ND), is governed by the energy difference between BCC and face-centred cubic (FCC) structures and χ robustly captures this key quantity. For BCC TMs alloys, the core structure transition from ND to D occurs when χ drops below a threshold, as seen in atomistic simulations based on nearly all extant interatomic potentials and density functional theory (DFT) calculations of W-Re/Ta alloys. In binary W-TMs alloys, DFT calculations show that χ is related to the valence electron concentration at low to moderate solute concentrations, and can be controlled via alloying. χ can be quantitatively and efficiently predicted via rapid, low-cost DFT calculations for any BCC metal alloys, providing a robust, easily applied tool for the design of ductile and tough BCC alloys. read less R. Namakian, B. R. Novak, X. Zhang, W. Meng, and D. Moldovan, “A combined molecular dynamics/Monte Carlo simulation of Cu thin film growth on TiN substrates: Illustration of growth mechanisms and comparison with experiments,” Applied Surface Science. 2021. link Times cited: 6 S. Starikov et al., “Angular-dependent interatomic potential for large-scale atomistic simulation of iron: Development and comprehensive comparison with existing interatomic models,” Physical Review Materials. 2021. link Times cited: 16 Abstract: The development of classical interatomic potential for iron … read more Abstract: The development of classical interatomic potential for iron is a quite demanding task with a long history background. A new interatomic potential for simulation of iron was created with a focus on description of crystal defects properties. In contrast with previous studies, here the potential development was based on force-matching method that requires only ab initio data as reference values. To verify our model, we studied various features of body-centered-cubic iron including the properties of point defects (vacancy and self-interstitial atom), the Peierls energy barrier for dislocations (screw and mix types), and the formation energies of planar defects (surfaces, grain boundaries, and stacking fault). The verification also implies thorough comparison of a potential with 11 other interatomic potentials reported in literature. This potential correctly reproduces the largest number of iron characteristics which ensures its advantage and wider applicability range compared to the other considered classical potentials. Here application of the model is illustrated by estimation of self-diffusion coefficients and the calculation of fcc lattice properties at high temperature. read less S. Kavousi, B. R. Novak, D. Moldovan, and M. A. Zaeem, “Quantitative prediction of rapid solidification by integrated atomistic and phase-field modeling,” Acta Materialia. 2021. link Times cited: 8 P. Nieves, J. Tranchida, S. Arapan, and D. Legut, “Spin-lattice model for cubic crystals,” Physical Review B. 2020. link Times cited: 11 Abstract: We present a methodology based on the N\'{e}el model to… read more Abstract: We present a methodology based on the N\'{e}el model to build a classical spin-lattice Hamiltonian for cubic crystals capable of describing magnetic properties induced by the spin-orbit coupling like magnetocrystalline anisotropy and anisotropic magnetostriction, as well as exchange magnetostriction. Taking advantage of the analytical solutions of the N\'{e}el model, we derive theoretical expressions for the parameterization of the exchange integrals and N\'{e}el dipole and quadrupole terms that link them to the magnetic properties of the material. This approach allows to build accurate spin-lattice models with the desire magnetoelastic properties. We also explore a possible way to model the volume dependence of magnetic moment based on the Landau energy. This new feature can allow to consider the effects of hydrostatic pressure on the saturation magnetization. We apply this method to develop a spin-lattice model for BCC Fe and FCC Ni, and we show that it accurately reproduces the experimental elastic tensor, magnetocrystalline anisotropy under pressure, anisotropic magnetostrictive coefficients, volume magnetostriction and saturation magnetization under pressure at zero-temperature. This work could constitute a step towards large-scale modeling of magnetoelastic phenomena. read less S. A. Etesami, M. Laradji, and E. Asadi, “Reliability of molecular dynamics interatomic potentials for modeling of titanium in additive manufacturing processes,” Computational Materials Science. 2020. link Times cited: 5 M. Xing, A. Pathak, S. Sanyal, Q. Peng, X. Liu, and X. Wen, “Temperature-dependent surface free energy and the Wulff shape of iron and iron carbide nanoparticles: A molecular dynamics study,” Applied Surface Science. 2020. link Times cited: 13 I. Aslam et al., “Thermodynamic and kinetic behavior of low-alloy steels: An atomic level study using an Fe-Mn-Si-C modified embedded atom method (MEAM) potential,” Materialia. 2019. link Times cited: 12 A. Mahata and M. A. Zaeem, “Effects of solidification defects on nanoscale mechanical properties of rapid directionally solidified Al-Cu Alloy: A large scale molecular dynamics study,” Journal of Crystal Growth. 2019. link Times cited: 21 N. T. Brown, E. Martínez, and J. Qu, “Solid-liquid metal interface definition studies using capillary fluctuation method,” Computational Materials Science. 2019. link Times cited: 3 K. Li et al., “Determination of the accuracy and reliability of molecular dynamics simulations in estimating the melting point of iron: Roles of interaction potentials and initial system configurations,” Journal of Molecular Liquids. 2019. link Times cited: 8 L. Gr’an’asy et al., “Phase-field modeling of crystal nucleation in undercooled liquids – A review,” Progress in Materials Science. 2019. link Times cited: 67 S. Kavousi, B. R. Novak, M. A. Zaeem, and D. Moldovan, “Combined molecular dynamics and phase field simulation investigations of crystal-melt interfacial properties and dendritic solidification of highly undercooled titanium,” Computational Materials Science. 2019. link Times cited: 28 A. Mahata and M. A. Zaeem, “Evolution of solidification defects in deformation of nano-polycrystalline aluminum,” Computational Materials Science. 2019. link Times cited: 25 S. A. Etesami, M. Baskes, M. Laradji, and E. Asadi, “Thermodynamics of solid Sn and Pb Sn liquid mixtures using molecular dynamics simulations,” Acta Materialia. 2018. link Times cited: 21 A. Nourian-Avval and E. Asadi, “Thermodynamics of FCC metals at melting point in one-mode phase-field crystals model,” Computational Materials Science. 2018. link Times cited: 7 M. A. Zaeem and E. Asadi, “Phase‐Field Crystal Modeling: Integrating Density Functional Theory, Molecular Dynamics, and Phase‐Field Modeling.” 2018. link Times cited: 2 A. Yamanaka, K. McReynolds, and P. Voorhees, “Phase field crystal simulation of grain boundary motion, grain rotation and dislocation reactions in a BCC bicrystal,” Acta Materialia. 2017. link Times cited: 51 A. Nourian-Avval and E. Asadi, “On the quantification of phase-field crystals model for computational simulations of solidification in metals,” Computational Materials Science. 2017. link Times cited: 11 C. Qi, J. Li, B. Xu, L. Kong, and S. Zhao, “Atomistic characterization of solid-liquid interfaces in the Cu-Ni binary alloy system,” Computational Materials Science. 2016. link Times cited: 15 E. Asadi and M. A. Zaeem, “Predicting Solidification Properties of Magnesium by Molecular Dynamics Simulations.” 2016. link Times cited: 0 Y. Shibuta, K. Oguchi, T. Takaki, and M. Ohno, “Homogeneous nucleation and microstructure evolution in million-atom molecular dynamics simulation,” Scientific Reports. 2015. link Times cited: 85 E. Asadi and M. A. Zaeem, “A Modified Two-Mode Phase-Field Crystal Model Applied to Face-Centered Cubic and Body-Centered Cubic Orderings,” Computational Materials Science. 2015. link Times cited: 26 A. Mahata, T. Mukhopadhyay, and M. A. Zaeem, “Liquid ordering induced heterogeneities in homogeneous nucleation during solidification of pure metals,” Journal of Materials Science & Technology. 2022. link Times cited: 11 S. A. Etesami and E. Asadi, “Molecular dynamics for near melting temperatures simulations of metals using modified embedded-atom method,” Journal of Physics and Chemistry of Solids. 2018. link Times cited: 71 J. Harrison, J. Schall, S. Maskey, P. Mikulski, M. T. Knippenberg, and B. Morrow, “Review of force fields and intermolecular potentials used in atomistic computational materials research,” Applied Physics Reviews. 2018. link Times cited: 99 Abstract: Molecular simulation is a powerful computational tool for a … read more Abstract: Molecular simulation is a powerful computational tool for a broad range of applications including the examination of materials properties and accelerating drug discovery. At the heart of molecular simulation is the analytic potential energy function. These functions span the range of complexity from very simple functions used to model generic phenomena to complex functions designed to model chemical reactions. The complexity of the mathematical function impacts the computational speed and is typically linked to the accuracy of the results obtained from simulations that utilize the function. One approach to improving accuracy is to simply add more parameters and additional complexity to the analytic function. This approach is typically used in non-reactive force fields where the functional form is not derived from quantum mechanical principles. The form of other types of potentials, such as the bond-order potentials, is based on quantum mechanics and has led to varying levels of accuracy and transferability. When selecting a potential energy function for use in molecular simulations, the accuracy, transferability, and computational speed must all be considered. In this focused review, some of the more commonly used potential energy functions for molecular simulations are reviewed with an eye toward presenting their general forms, strengths, and weaknesses.Molecular simulation is a powerful computational tool for a broad range of applications including the examination of materials properties and accelerating drug discovery. At the heart of molecular simulation is the analytic potential energy function. These functions span the range of complexity from very simple functions used to model generic phenomena to complex functions designed to model chemical reactions. The complexity of the mathematical function impacts the computational speed and is typically linked to the accuracy of the results obtained from simulations that utilize the function. One approach to improving accuracy is to simply add more parameters and additional complexity to the analytic function. This approach is typically used in non-reactive force fields where the functional form is not derived from quantum mechanical principles. The form of other types of potentials, such as the bond-order potentials, is based on quantum mechanics and has led to varying levels of accuracy and transferabilit... read less S. Ghosh, “A novel technique to obtain analytical direct correlation functions for use in classical density functional theory,” Computational Materials Science. 2017. link Times cited: 0 D. Tourret, H. Liu, and J. Llorca, “Phase-field modeling of microstructure evolution: Recent applications, perspectives and challenges,” Progress in Materials Science. 2021. link Times cited: 61 S. Kazanç and C. Canbay, “Fe elementindeki αγδ Katı-Katı Faz Geçişlerinin Moleküler Dinamik Benzetimi ile İncelenmesi,” Fırat Üniversitesi Mühendislik Bilimleri Dergisi. 2021. link Times cited: 0 Abstract: When the phase diagram of the element Fe is examined, it is … read more Abstract: When the phase diagram of the element Fe is examined, it is seen that it has different crystal structures at different temperatures below its melting temperature. In this study, the solid-solid phase transformations occurring at different temperatures in the Fe model system consisting of 4000 atoms were investigated using molecular dynamic simulation method. The Embedded Atom Method(EAM), which includes many body interactions, was used to calculate interactions between atoms. For the element Fe, the α, γ and δ phases formed below the melting temperature and the transformation temperatures for these phases were determined and the results were compared with the experimental values. Radial distribution function, changes in thermodynamic quantities and Ackland-Jones analysis method were used in the structural analysis of the model system. read less T. D. Cuong and A. D. Phan, “Efficient analytical approach for high-pressure melting properties of iron.” 2020. link Times cited: 8 S. Kavousi, B. R. Novak, M. Baskes, M. A. Zaeem, and D. Moldovan, “Modified embedded-atom method potential for high-temperature crystal-melt properties of Ti–Ni alloys and its application to phase field simulation of solidification,” Modelling and Simulation in Materials Science and Engineering. 2019. link Times cited: 21 Abstract: We developed new interatomic potentials, based on the second… read more Abstract: We developed new interatomic potentials, based on the second nearest-neighbor modified embedded-atom method (2NN-MEAM) formalism, for Ti, Ni, and the binary Ti–Ni system. These potentials were fit to melting points, latent heats, the binary phase diagrams for the Ti rich and Ni rich regions, and the liquid phase enthalpy of mixing for binary alloys, therefore they are particularly suited for calculations of crystal-melt (CM) interface thermodynamic and transport properties. The accuracy of the potentials for pure Ti and pure Ni were tested against both 0 K and high temperature properties by comparing various properties obtained from experiments or density functional theory calculations including structural properties, elastic constants, point-defect properties, surface energies, temperatures and enthalpies of phase transformations, and diffusivity and viscosity in the liquid phase. The fitted binary potential for Ti–Ni was also tested against various non-fitted properties at 0 K and high temperatures including lattice parameters, formation energies of different intermetallic compounds, and the temperature dependence of liquid density at various concentrations. The CM interfacial free energies obtained from simulations, based on the newly developed Ti–Ni potential, show that the bcc alloys tend to have smaller anisotropy compared with fcc alloys which is consistent with the finding from the previous studies comparing single component bcc and fcc materials. Moreover, the interfacial free energy and its anisotropy for Ti-2 atom% Ni were also used to parameterize a 2D phase field (PF) model utilized in solidification simulations. The PF simulation predictions of microstructure development during solidification are in good agreement with a geometric model for dendrite primary arm spacing. read less A. Mahata and M. A. Zaeem, “Size effect in molecular dynamics simulation of nucleation process during solidification of pure metals: investigating modified embedded atom method interatomic potentials,” Modelling and Simulation in Materials Science and Engineering. 2019. link Times cited: 8 Abstract: Due to the significant increase in computing power in recent… read more Abstract: Due to the significant increase in computing power in recent years, the simulation size of atomistic methods for studying the nucleation process during solidification has been gradually increased, even to billion atom simulations (sub-micron length scale). But the question is how big of a model is required for size-independent and accurate simulations of the nucleation process during solidification? In this work, molecular dynamics simulations with model sizes ranging from ∼2000 to ∼8 million atoms were used to study nucleation during solidification. To draw general conclusions independent of crystal structures, the most advanced second nearest-neighbor modified embedded atom method interatomic potentials for Al (face-centered cubic), Fe (body-centered cubic), and Mg (hexagonal-close packed) were utilized for molecular dynamics simulations. We have analyzed several quantitative characteristics such as nucleation time, density of nuclei, nucleation rate, self-diffusion coefficient, and change in free energy during solidification. The results showed that by increasing the model size to about two million atoms, the simulations and measurable quantities become entirely independent of simulation cell size. The prediction of cell size required for size-independent computed data can considerably reduce the computational costs of atomistic simulations and at the same time increase the accuracy and reliability of the computational data. read less S. M. Handrigan, L. Morrissey, and S. Nakhla, “Investigating various many-body force fields for their ability to predict reduction in elastic modulus due to vacancies using molecular dynamics simulations,” Molecular Simulation. 2019. link Times cited: 6 Abstract: ABSTRACT Molecular dynamics simulations are more frequently … read more Abstract: ABSTRACT Molecular dynamics simulations are more frequently being utilised to predict macroscale mechanical properties as a result of atomistic defects. However, the interatomic force field can significantly affect the resulting mechanical properties. While several studies exist which demonstrate the ability of various force fields to predict mechanical properties, the investigation into which is most accurate for the investigation of vacancies is limited. To obtain meaningful predictions of mechanical properties, a clear understanding of force field parameterisation is required. As such, the current study evaluates various many-body force fields to demonstrate the reduction in mechanical properties of iron and iron–chromium due to the presence of vacancies while undergoing room temperature atomistic uniaxial tension. Reduction was normalised in each case with the zero-vacancy elastic modulus, removing the need to predict an accurate nominal elastic modulus. Comparisons were made to experimental data and an empirical model from literature. It was demonstrated that accurate fitting to vacancy formation and migration energy allowed for accurate predictions. In addition, bond-order based force fields showed enhanced predictions regardless of fitting procedure. Overall, these findings highlight the need to understand capabilities and limitations of available force fields, as well as the need for enhanced parameterisation of force fields. read less L. Morrissey, S. M. Handrigan, S. Subedi, and S. Nakhla, “Atomistic uniaxial tension tests: investigating various many-body potentials for their ability to produce accurate stress strain curves using molecular dynamics simulations,” Molecular Simulation. 2019. link Times cited: 13 Abstract: ABSTRACT Molecular dynamics simulations, which take place on… read more Abstract: ABSTRACT Molecular dynamics simulations, which take place on the atomistic scale, are now being used to predict the influence of atomistic processes on macro-scale mechanical properties. However, there is a lack of clear understanding on which potential should be used when attempting to obtain these properties. Moreover, many MD studies that do test mechanical properties do not actually simulate the macro-scale laboratory tension tests used to obtain them. As such, the purpose of the current study was to evaluate the various types of potentials for their accuracy in predicting the mechanical properties of iron from an atomistic uniaxial tension test at room temperature. Results demonstrated that while EAM and MEAM potentials all under predicted the elastic modulus at room temperature, the Tersoff and ReaxFF potentials were significantly more accurate. Unlike EAM and MEAM, both the Tersoff and ReaxFF potentials are bond order based. Therefore, these results demonstrate the importance of considering bonding between atoms when modelling tensile tests. In addition, the ReaxFF potential also accurately predicted the Poisson's ratio, allowing for complete characterisation of the material's behaviour. Overall, these findings highlight the need to understand the capabilities and limitations of each potential before application to a problem outside of the initial intended use. read less K. A. Moats, E. Asadi, and M. Laradji, “Phase field crystal simulations of the kinetics of Ostwald ripening in two dimensions.,” Physical review. E. 2019. link Times cited: 8 Abstract: The kinetics of Ostwald ripening of solid domains in the liq… read more Abstract: The kinetics of Ostwald ripening of solid domains in the liquid phase of one-component systems in two dimensions is investigated numerically via the phase field crystal model. The simulations, which are performed systematically as a function of volume fraction of the solid phase, show that dynamical scaling is reached during late times, and the growth law is in good agreement with the classical theory of Lifshitz, Slyozov, and Wagner (LSW), i.e., R[over ¯]∼t^{1/3}, an indication that domain growth is mediated by the long-range interdomain diffusion of atoms. In contrast to the LSW theory, however, the domain size distribution is symmetric, and can be fit with a Gaussian. The investigation of the topological domain structure, through the Voronoi tessellation of the domains' centers of mass shows that both the Lewis law and the Aboav-Weaire law of two-dimensional cellular patterns are satisfied, implying that the kinetics proceed such that the conformational entropy of the domain-containing Voronoi cells is maximized. These results are in very good agreement with an earlier experimental study of a phase-separating phospholipid-cholesterol Langmuir film. read less S. A. Etesami, M. Laradji, and E. Asadi, “Transferability of interatomic potentials in predicting the temperature dependency of elastic constants for titanium, zirconium and magnesium,” Modelling and Simulation in Materials Science and Engineering. 2019. link Times cited: 4 Abstract: We present our investigation of the current state of the art… read more Abstract: We present our investigation of the current state of the art for the transferability of molecular dynamics (MD) interatomic potentials for high temperature simulations of material processes in terms of elastic constants. With the current advancement of computer power, nanoscale computational models such as MD have the potential to accelerate optimization and development of high temperature material processes provided a robust and transferable interatomic potential. Temperature dependency of elastic constants, despite the low temperature elastic constants, is not commonly used as one of the target material properties to develop interatomic potentials for metals; thus, it is a reliable index to determine the transferability of interatomic potentials for high temperature simulations. We consider all five independent elastic constants and their temperature dependency as an index for our evaluations of available interatomic potentials for titanium (Ti), zirconium (Zr), and magnesium (Mg) as representative metals with a relatively complex crystal structure (hcp). The calculated elastic constants and their deviation from their corresponding experimental values are presented. We provide a through discussion on the transferability of each potential and summarize with the most suitable potentials for high temperature material process simulations for each considered material. read less C. Angelie and J. Soudan, “Nanothermodynamics of iron clusters: Small clusters, icosahedral and fcc-cuboctahedral structures.,” The Journal of chemical physics. 2017. link Times cited: 3 Abstract: The study of the thermodynamics and structures of iron clust… read more Abstract: The study of the thermodynamics and structures of iron clusters has been carried on, focusing on small clusters and initial icosahedral and fcc-cuboctahedral structures. Two combined tools are used. First, energy intervals are explored by the Monte Carlo algorithm, called σ-mapping, detailed in the work of Soudan et al. [J. Chem. Phys. 135, 144109 (2011), Paper I]. In its flat histogram version, it provides the classical density of states, gp(Ep), in terms of the potential energy of the system. Second, the iron system is described by a potential which is called "corrected EAM" (cEAM), explained in the work of Basire et al. [J. Chem. Phys. 141, 104304 (2014), Paper II]. Small clusters from 3 to 12 atoms in their ground state have been compared first with published Density Functional Theory (DFT) calculations, giving a complete agreement of geometries. The series of 13, 55, 147, and 309 atom icosahedrons is shown to be the most stable form for the cEAM potential. However, the 147 atom cluster has a special behaviour, since decreasing the energy from the liquid zone leads to the irreversible trapping of the cluster in a reproducible amorphous state, 7.38 eV higher in energy than the icosahedron. This behaviour is not observed at the higher size of 309 atoms. The heat capacity of the 55, 147, and 309 atom clusters revealed a pronounced peak in the solid zone, related to a solid-solid transition, prior to the melting peak. The corresponding series of 13, 55, and 147 atom cuboctahedrons has been compared, underscoring the unstability towards the icosahedral structure. This unstability occurs clearly in several steps for the 147 atom cluster, with a sudden transformation at a transition state. This illustrates the concerted icosahedron-cuboctahedron transformation of Buckminster Fuller-Mackay, which is calculated for the cEAM potential. Two other clusters of initial fcc structures with 24 and 38 atoms have been studied, as well as a 302 atom cluster. Each one relaxes towards a more stable structure without regularity. The 38 atom cluster exhibits a nearly glassy relaxation, through a cascade of six metastable states of long life. This behaviour, as that of the 147 atom cluster towards the amorphous state, shows that difficulties to reach ergodicity in the lower half of the solid zone are related to particular features of the potential energy landscape, and not necessarily to a too large size of the system. Comparisons of the cEAM iron system with published results about Lennard-Jones systems and DFT calculations are made. The results of the previous clusters have been combined with that of Paper II to plot the cohesive energy Ec and the melting temperature Tm in terms of the cluster atom number Nat. The Nat-1/3 linear dependence of the melting temperature (Pawlow law) is observed again for Nat > 150. In contrast, for Nat < 150, the curve diverges strongly from the Pawlow law, giving it an overall V-shape, with a linear increase of Tm when Nat goes from 55 to 13 atoms. Surprisingly, the 38 atom cluster is anomalously below the overall curve. read less E. Asadi and M. A. Zaeem, “Quantitative Phase-Field Crystal Modeling of Solid-Liquid Interfaces for FCC Metals,” Computational Materials Science. 2017. link Times cited: 8 C. Guo, J. Wang, Z. Wang, J. Li, Y. Guo, and Y. Huang, “Interfacial free energy adjustable phase field crystal model for homogeneous nucleation.,” Soft matter. 2016. link Times cited: 13 Abstract: To describe the homogeneous nucleation process, an interfaci… read more Abstract: To describe the homogeneous nucleation process, an interfacial free energy adjustable phase-field crystal model (IPFC) was proposed by reconstructing the energy functional of the original phase field crystal (PFC) methodology. Compared with the original PFC model, the additional interface term in the IPFC model effectively can adjust the magnitude of the interfacial free energy, but does not affect the equilibrium phase diagram and the interfacial energy anisotropy. The IPFC model overcame the limitation that the interfacial free energy of the original PFC model is much less than the theoretical results. Using the IPFC model, we investigated some basic issues in homogeneous nucleation. From the viewpoint of simulation, we proceeded with an in situ observation of the process of cluster fluctuation and obtained quite similar snapshots to colloidal crystallization experiments. We also counted the size distribution of crystal-like clusters and the nucleation rate. Our simulations show that the size distribution is independent of the evolution time, and the nucleation rate remains constant after a period of relaxation, which are consistent with experimental observations. The linear relation between logarithmic nucleation rate and reciprocal driving force also conforms to the steady state nucleation theory. read less A. Emdadi, M. A. Zaeem, and E. Asadi, “Revisiting Phase Diagrams of Two-Mode Phase-Field Crystal Models,” Computational Materials Science. 2016. link Times cited: 18 Y. Shibuta, M. Ohno, and T. Takaki, “Solidification in a Supercomputer: From Crystal Nuclei to Dendrite Assemblages,” JOM. 2015. link Times cited: 83 M. A. Zaeem, “Recent Advances in Study of Solid-Liquid Interfaces and Solidification of Metals.” 2018. link Times cited: 1 Abstract: Solidification occurs in several material processing methods… read more Abstract: Solidification occurs in several material processing methods, such as in casting, welding, and laser additive manufacturing of metals, and it controls the nano- and microstructures, as well as the overall properties of the products[...] read less
Funding	Not available

Short KIM ID The unique KIM identifier code.	SM_042630680993_001
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.	Sim_LAMMPS_MEAM_AsadiZaeemNouranian_2015_Fe__SM_042630680993_001
DOI	10.25950/fa2eccc9 https://doi.org/10.25950/fa2eccc9 https://commons.datacite.org/doi.org/10.25950/fa2eccc9
KIM Item Type	Simulator Model
KIM API Version	2.2
Simulator Name The name of the simulator as defined in kimspec.edn.	LAMMPS
Potential Type	meam
Simulator Potential	meam
Run Compatibility	portable-models

Previous Version	Sim_LAMMPS_MEAM_AsadiZaeemNouranian_2015_Fe__SM_042630680993_000

Verification Check Dashboard

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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
N/A Unable to perform verification check due to an error.	vc-periodicity-support	mandatory	Periodic boundary conditions are handled correctly; see full description.	Results	Files
A The letter grade A was assigned because the normalized error in the computation was 2.30959e-09 compared with a machine precision of 2.22045e-16. The letter grade was based on 'score=log10(error/eps)', with ranges A=[0, 7.5], B=(7.5, 10.0], C=(10.0, 12.5], D=(12.5, 15.0), F>15.0. 'A' is the best grade, and 'F' indicates failure.	vc-forces-numerical-derivative	consistency	Forces computed by the model agree with numerical derivatives of the energy; see full description.	Results	Files
F The model is C^0 continuous. This means that the model has continuous energy, but a discontinuous first derivative.	vc-dimer-continuity-c1	informational	The energy versus separation relation of a pair of atoms is C1 continuous (i.e. the function and its first derivative are continuous); see full description.	Results	Files
F Memory leak detected.	vc-memory-leak	informational	The model code does not have memory leaks (i.e. it releases all allocated memory at the end); see full description.	Results	Files
N/A Thread safety can only be checked for KIM Models.	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

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: Fe

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.

(No matching species)

Diamond Lattice Constant

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

Species: Fe

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.

(No matching species)

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: Fe

FCC Stacking Fault Energies

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

(No matching species)

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.

(No matching species)

SC Lattice Constant

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

Species: Fe

Cubic Crystal Basic Properties Table

Species: Fe

Tests

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 Fe in AFLOW crystal prototype A_cF4_225_a v003	view	373735
Equilibrium crystal structure and energy for Fe in AFLOW crystal prototype A_cI2_229_a v003	view	190567
Equilibrium crystal structure and energy for Fe in AFLOW crystal prototype A_hP2_194_c v003	view	191288
Equilibrium crystal structure and energy for Fe in AFLOW crystal prototype A_tP28_136_f2ij v003	view	599085

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 Fe v007	view	9276
Equilibrium zero-temperature lattice constant for diamond Fe v007	view	18184
Equilibrium zero-temperature lattice constant for fcc Fe v007	view	15313
Equilibrium zero-temperature lattice constant for sc Fe v007	view	25178

Errors

EquilibriumCrystalStructure__TD_457028483760_003

Test	Error Categories	Link to Error page
Equilibrium crystal structure and energy for Fe in AFLOW crystal prototype A_tP1_123_a v003	other	view

LatticeConstantCubicEnergy__TD_475411767977_007

Test	Error Categories	Link to Error page
Equilibrium zero-temperature lattice constant for bcc Fe v007	other	view
Equilibrium zero-temperature lattice constant for diamond Fe v007	other	view
Equilibrium zero-temperature lattice constant for fcc Fe v007	other	view
Equilibrium zero-temperature lattice constant for sc Fe v007	other	view

LatticeConstantHexagonalEnergy__TD_942334626465_005

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

No Driver

Verification Check	Error Categories	Link to Error page
InversionSymmetry__VC_021653764022_002	other	view
MemoryLeak__VC_561022993723_004	other	view
Objectivity__VC_813478999433_002	other	view
PeriodicitySupport__VC_895061507745_004	other	view
PermutationSymmetry__VC_903502816694_002	other	view

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CMakeLists.txt
Fe.meam
LICENSE
kimprovenance.edn
kimspec.edn
library.Fe.meam
smspec.edn

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Sim_LAMMPS_MEAM_AsadiZaeemNouranian_2015_Fe__SM_042630680993_001

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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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