EAM potential (LAMMPS cubic hermite tabulation) for the Pt-Au system developed by O'Brien et al. (2018) v000

Christopher J O'Brien; Christopher M Barr; Khalid Hattar; Patrick M Price; Stephen M. Foiles

doi:10.25950/a57b36d8

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EAM_Dynamo_OBrienBarrPrice_2018_PtAu__MO_946831081299_000

Interatomic potential for Gold (Au), Platinum (Pt).
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Title A single sentence description.	EAM potential (LAMMPS cubic hermite tabulation) for the Pt-Au system developed by O'Brien et al. (2018) v000
Description A short description of the Model describing its key features including for example: type of model (pair potential, 3-body potential, EAM, etc.), modeled species (Ac, Ag, ..., Zr), intended purpose, origin, and so on.	EAM potential for the Pt-Au system developed by O'Brien et al. (2018). There has recently been a great deal of interest in employing immiscible solutes to stabilize nanocrystalline microstructures. Existing modeling efforts largely rely on mesoscale Monte Carlo approaches that employ a simplified model of the microstructure and result in highly homogeneous segregation to grain boundaries. However, there is ample evidence from experimental and modeling studies that demonstrates segregation to grain boundaries is highly non-uniform and sensitive to boundary character. This work employs a realistic nanocrystalline microstructure with experimentally relevant global solute concentrations to illustrate inhomogeneous boundary segregation. Experiments quantifying segregation in thin films are reported that corroborate the prediction that grain boundary segregation is highly inhomogeneous. In addition to grain boundary structure modifying the degree of segregation, the existence of a phase transformation between low and high solute content grain boundaries is predicted. In order to conduct this study, new embedded atom method interatomic potentials are developed for Pt, Au, and the PtAu binary alloy.
Species The supported atomic species.	Au, Pt
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/Pt.html#Au-Pt)

Contributor	Ellad B. Tadmor
Maintainer	Ellad B. Tadmor
Developer	Christopher J O'Brien Christopher M Barr Khalid Hattar Patrick M Price Stephen M. Foiles
Published on KIM	2018
How to Cite	This Model originally published in [1] is archived in OpenKIM [2-5]. [1] O’Brien CJ, Barr CM, Price PM, Hattar K, Foiles SM. Grain boundary phase transformations in PtAu and relevance to thermal stabilization of bulk nanocrystalline metals. Journal of Materials Science. 2018Feb;53(4):2911–27. doi:10.1007/s10853-017-1706-1 — (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] O’Brien CJ, Barr CM, Hattar K, Price PM, Foiles SM. EAM potential (LAMMPS cubic hermite tabulation) for the Pt-Au system developed by O’Brien et al. (2018) v000. OpenKIM; 2018. doi:10.25950/a57b36d8 [3] Foiles SM, Baskes MI, Daw MS, Plimpton SJ. EAM Model Driver for tabulated potentials with cubic Hermite spline interpolation as used in LAMMPS v005. OpenKIM; 2018. doi:10.25950/68defa36 [4] Tadmor EB, Elliott RS, Sethna JP, Miller RE, Becker CA. The potential of atomistic simulations and the Knowledgebase of Interatomic Models. JOM. 2011;63(7):17. doi:10.1007/s11837-011-0102-6 [5] Elliott RS, Tadmor EB. Knowledgebase of Interatomic Models (KIM) Application Programming Interface (API). OpenKIM; 2011. doi:10.25950/ff8f563a Click here to download the above citation in BibTeX format.
Citations This panel presents information regarding the papers that have cited the interatomic potential (IP) whose page you are on. The OpenKIM machine learning based Deep Citation framework is used to determine whether the citing article actually used the IP in computations (denoted by "USED") or only provides it as a background citation (denoted by "NOT USED"). For more details on Deep Citation and how to work with this panel, click the documentation link at the top of the panel. The word cloud to the right is generated from the abstracts of IP principle source(s) (given below in "How to Cite") and the citing articles that were determined to have used the IP in order to provide users with a quick sense of the types of physical phenomena to which this IP is applied. The bar chart shows the number of articles that cited the IP per year. Each bar is divided into green (articles that USED the IP) and blue (articles that did NOT USE the IP). Users are encouraged to correct Deep Citation errors in determination by clicking the speech icon next to a citing article and providing updated information. This will be integrated into the next Deep Citation learning cycle, which occurs on a regular basis. OpenKIM acknowledges the support of the Allen Institute for AI through the Semantic Scholar project for providing citation information and full text of articles when available, which are used to train the Deep Citation ML algorithm.	This panel provides information on past usage of this interatomic potential (IP) powered by the OpenKIM Deep Citation framework. The word cloud indicates typical applications of the potential. The bar chart shows citations per year of this IP (bars are divided into articles that used the IP (green) and those that did not (blue)). The complete list of articles that cited this IP is provided below along with the Deep Citation determination on usage. See the Deep Citation documentation for more information. 55 Citations (39 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 . X. Zhou et al., “The hidden structure dependence of the chemical life of dislocations,” Science Advances. 2021. link Times cited: 25 Abstract: Combined atomic-scale characterization and simulation reveal… read more Abstract: Combined atomic-scale characterization and simulation reveal the complexity and diversity of the chemical nature of dislocations. Dislocations are one-dimensional defects in crystals, enabling their deformation, mechanical response, and transport properties. Less well known is their influence on material chemistry. The severe lattice distortion at these defects drives solute segregation to them, resulting in strong, localized spatial variations in chemistry that determine microstructure and material behavior. Recent advances in atomic-scale characterization methods have made it possible to quantitatively resolve defect types and segregation chemistry. As shown here for a Pt-Au model alloy, we observe a wide range of defect-specific solute (Au) decoration patterns of much greater variety and complexity than expected from the Cottrell cloud picture. The solute decoration of the dislocations can be up to half an order of magnitude higher than expected from classical theory, and the differences are determined by their structure, mutual alignment, and distortion field. This opens up pathways to use dislocations for the compositional and structural nanoscale design of advanced materials. read less C. Barr et al., “The role of grain boundary character in solute segregation and thermal stability of nanocrystalline Pt-Au.,” Nanoscale. 2021. link Times cited: 24 Abstract: Nanocrystalline (NC) metals suffer from an intrinsic thermal… read more Abstract: Nanocrystalline (NC) metals suffer from an intrinsic thermal instability; their crystalline grains undergo rapid coarsening during processing treatments or under service conditions. Grain boundary (GB) solute segregation has been proposed to mitigate grain growth and thermally stabilize the grain structures of NC metals. However, the role of GB character in solute segregation and thermal stability of NC metals remains poorly understood. Herein, we employ high resolution microscopy techniques, atomistic simulations, and theoretical analysis to investigate and characterize the impact of GB character on segregation behavior and thermal stability in a model NC Pt-Au alloy. High resolution electron microscopy along with X-ray energy dispersive spectroscopy and automated crystallographic orientation mapping is used to obtain spatially correlated Pt crystal orientation, GB misorientation, and Au solute concentration data. Atomistic simulations of polycrystalline Pt-Au systems are used to reveal the plethora of GB segregation profiles as a function of GB misorientation and the corresponding impact on grain growth processes. With the aid of theoretical models of interface segregation, the experimental data for GB concentration profiles are used to extract GB segregation energies, which are then used to elucidate the impact of GB character on solute drag effects. Our results highlight the paramount role of GB character in solute segregation behavior. In broad terms, our approach provides future avenues to employ GB segregation as a microstructure design strategy to develop NC metallic alloys with tailored microstructures. read less M. Bagheripoor and R. Klassen, “Effect of crystal orientation on the size effects of nano-scale fcc metals,” Materials Science and Technology. 2020. link Times cited: 3 Abstract: The present work investigates the dominant mechanisms in the… read more Abstract: The present work investigates the dominant mechanisms in the plasticity of nano-sized fcc metallic samples. Molecular dynamics simulations of nanopillar compression show that plasticity always starts with the nucleation of dislocations at the free surface, and the crystal orientation affects the subsequent microstructural evolution. The Schmid factor of leading and trailing partials plays a decisive role in leading to the twinning, or slip deformation. A significant difference is observed in the strength of pillars of the same size with different orientations. The power-law equation exponent is completely dependent on the crystal orientations, and a weak or no size effect is observed in the compression of [100]- and [110]-oriented nanopillars. The observed orientation based behaviour decreases by confining the free surface. read less M. Wagih, P. M. Larsen, and C. Schuh, “Learning grain boundary segregation energy spectra in polycrystals,” Nature Communications. 2020. link Times cited: 62 N. Heckman et al., “New nanoscale toughening mechanisms mitigate embrittlement in binary nanocrystalline alloys.,” Nanoscale. 2018. link Times cited: 24 Abstract: Nanocrystalline metals offer significant improvements in str… read more Abstract: Nanocrystalline metals offer significant improvements in structural performance over conventional alloys. However, their performance is limited by grain boundary instability and limited ductility. Solute segregation has been proposed as a stabilization mechanism, however the solute atoms can embrittle grain boundaries and further degrade the toughness. In the present study, we confirm the embrittling effect of solute segregation in Pt-Au alloys. However, more importantly, we show that inhomogeneous chemical segregation to the grain boundary can lead to a new toughening mechanism termed compositional crack arrest. Energy dissipation is facilitated by the formation of nanocrack networks formed when cracks arrested at regions of the grain boundaries that were starved in the embrittling element. This mechanism, in concert with triple junction crack arrest, provides pathways to optimize both thermal stability and energy dissipation. A combination of in situ tensile deformation experiments and molecular dynamics simulations elucidate both the embrittling and toughening processes that can occur as a function of solute content. read less Y. Mahmood, M. S. Daw, M. Chandross, and F. Abdeljawad, “Universal trends in computed grain boundary energies of FCC metals,” Scripta Materialia. 2024. link Times cited: 0 J. Niu, R. Hu, X. Luo, Z. Gao, and P. Li, “Study on the Microscopic Compression Deformation Mechanism of Nanocrystalline Single-phase Gold Platinum Alloy,” Journal of Materials Research and Technology. 2023. link Times cited: 0 G. Luo, Y. Tian, F. Chen, J. Liu, S. Chen, and C. Du, “Atomic insight into mechanical behavior of AuPt alloys,” International Journal of Mechanical Sciences. 2023. link Times cited: 0 T. Pradhan, S. Pal, and C. Deng, “Investigation on wear-resistance of nanocrystalline Pt-Au by molecular dynamics simulations,” Tribology International. 2023. link Times cited: 0 C. Liu, Z. Zhuang, J. Chen, W. Yip, and S. To, “Atomic-scale investigation of Pt composition on deformation mechanism of AuPt alloy during nano-scratching process,” Surfaces and Interfaces. 2023. link Times cited: 1 N. Tuchinda and C. Schuh, “Triple junction solute segregation in Al-based polycrystals,” Physical Review Materials. 2023. link Times cited: 3 C. Hu, S. Berbenni, D. Medlin, and R. Dingreville, “Discontinuous segregation patterning across disconnections,” Acta Materialia. 2023. link Times cited: 3 N. Tuchinda and C. Schuh, “The Vibrational Entropy Spectra of Grain Boundary Segregation in Polycrystals,” Acta Materialia. 2022. link Times cited: 7 X.-W. Chen, R. Huang, and Y. Wen, “Phase segregation in bimetallic nanodroplets,” Journal of Materials Science. 2022. link Times cited: 0 A. Hernandez and T. Mueller, “Generalizability of Functional Forms for Interatomic Potential Models Discovered by Symbolic Regression,” ArXiv. 2022. link Times cited: 0 Abstract: In recent years there has been great progress in the use of … read more Abstract: In recent years there has been great progress in the use of machine learning algorithms to develop interatomic potential models. Machine-learned potential models are typically orders of magnitude faster than density functional theory but also orders of magnitude slower than physics-derived models such as the embedded atom method. In our previous work, we used symbolic regression to develop fast, accurate and transferrable interatomic potential models for copper with novel functional forms that resemble those of the embedded atom method. To determine the extent to which the success of these forms was specific to copper, here we explore the generalizability of these models to other face-centered cubic transition metals and analyze their out-of-sample performance on several material properties. We found that these forms work particularly well on elements that are chemically similar to copper. When compared to optimized Sutton-Chen models, which have similar complexity, the functional forms discovered using symbolic regression perform better across all elements considered except gold where they have a similar performance. They perform similarly to a moderately more complex embedded atom form on properties on which they were trained, and they are more accurate on average on other properties. We attribute this improved generalized accuracy to the relative simplicity of the models discovered using symbolic regression. The genetic programming models are found to outperform other models from the literature about 50% of the time in a variety of property predictions, with about 1/10th the model complexity on average. We discuss the implications of these results to the broader application of symbolic regression to the development of new potentials and highlight how models discovered for one element can be used to seed new searches for different elements. read less E.-A. Picard and F. Sansoz, “Ni Solute Segregation and Associated Plastic Deformation Mechanisms into Random FCC Ag, BCC Nb and HCP Zr Polycrystals,” Acta Materialia. 2022. link Times cited: 5 H. Jo et al., “Direct strain correlations at the single-atom level in three-dimensional core-shell interface structures,” Nature Communications. 2022. link Times cited: 8 A. Pedrazo-Tardajos et al., “Thermal Activation of Gold Atom Diffusion in Au@Pt Nanorods.,” ACS nano. 2022. link Times cited: 7 Abstract: Understanding the thermal stability of bimetallic nanopartic… read more Abstract: Understanding the thermal stability of bimetallic nanoparticles is of vital importance to preserve their functionalities during their use in a variety of applications. In contrast to well-studied bimetallic systems such as Au@Ag, heat-induced morphological and compositional changes in Au@Pt nanoparticles are insufficiently understood, even though Au@Pt is an important material for catalysis. To investigate the thermal instability of Au@Pt nanorods at temperatures below their bulk melting point, we combined in situ heating with two- and three-dimensional electron microscopy techniques, including three-dimensional energy-dispersive X-ray spectroscopy. The experimental results were used as input for molecular dynamics simulations, to unravel the mechanisms behind the morphological transformation of Au@Pt core-shell nanorods. We conclude that thermal stability is influenced not only by the degree of coverage of Pt on Au but also by structural details of the Pt shell. read less L. Cao and T. Mueller, “Catalytic Activity Maps for Alloy Nanoparticles.,” Journal of the American Chemical Society. 2022. link Times cited: 6 Abstract: To enable rational design of alloy nanoparticle catalysts, w… read more Abstract: To enable rational design of alloy nanoparticle catalysts, we develop an approach to generate catalytic activity maps of alloy nanoparticles on a grid of particle size and composition. The catalytic activity maps are created by using a quaternary cluster expansion to explicitly predict adsorbate binding energies on alloy nanoparticles of varying shape, size, and atomic order while accounting for interactions among the adsorbates. This cluster expansion is used in kinetic Monte Carlo simulations to predict activated nanoparticle structures and turnover frequencies on all surface sites. We demonstrate our approach on Pt-Ni octahedral nanoparticle catalysts for the oxygen reduction reaction (ORR), revealing that the specific activity is predicted to be optimized at an edge length of larger than 5.5 nm and a composition of about Pt0.85Ni0.15 and the mass activity is predicted to be optimized at an edge length of 3.3-3.8 nm and a composition of about Pt0.8Ni0.2. read less N. Heckman et al., “Solute segregation improves the high-cycle fatigue resistance of nanocrystalline Pt-Au,” Acta Materialia. 2022. link Times cited: 4 T. Prithiv et al., “Grain boundary segregation and its implications regarding the formation of the grain boundary α phase in the metastable β-Titanium Ti–5Al–5Mo–5V–3Cr alloy,” Scripta Materialia. 2022. link Times cited: 18 N. Tuchinda and C. Schuh, “Grain Size Dependencies of Intergranular Solute Segregation in Nanocrystalline Materials,” Acta Materialia. 2021. link Times cited: 15 F. Torre et al., “Investigation on the Thermodynamic Stability of Nanocrystalline W-Based Alloys: A Combined Theoretical and Experimental Approach,” Materials. 2021. link Times cited: 1 Abstract: The stability of nanostructured metal alloys is currently be… read more Abstract: The stability of nanostructured metal alloys is currently being extensively investigated, and several mathematical models have been developed to describe the thermodynamics of these systems. However, model capability in terms of thermal stability predictions strongly relies on grain boundary-related parameters that are difficult to measure or estimate accurately. To overcome this limitation, a novel theoretical approach is proposed and adopted in this work to identify W-based nanocrystalline alloys which are potentially able to show thermodynamic stability. A comparison between model outcomes and experimental findings is reported for two selected alloys, namely W-Ag and W-Al. Experimental results clearly highlight that W-Ag mixtures retain a segregated structure on relatively coarse length scales even after prolonged mechanical treatments. Moreover, annealing at moderate temperatures readily induces demixing of the constituent elements. In contrast, homogeneous nanostructured W-Al solid solutions are obtained by ball milling of elemental powders. These alloys show enhanced thermal stability with respect to pure W even at high homologous temperatures. Experimental evidences agree with model predictions for both the investigated systems. read less M. Wagih and C. Schuh, “Thermodynamics and design of nanocrystalline alloys using grain boundary segregation spectra,” Acta Materialia. 2021. link Times cited: 20 X. Zhou, R. D. Kamachali, B. Boyce, B. G. Clark, D. Raabe, and G. Thompson, “Spinodal Decomposition in Nanocrystalline Alloys,” Acta Materialia. 2021. link Times cited: 25 C. Hu, D. Medlin, and R. Dingreville, “Disconnection-Mediated Transition in Segregation Structures at Twin Boundaries.,” The journal of physical chemistry letters. 2021. link Times cited: 4 Abstract: Twin boundaries play an important role in the thermodynamics… read more Abstract: Twin boundaries play an important role in the thermodynamics, stability, and mechanical properties of nanocrystalline metals. Understanding their structure and chemistry at the atomic scale is key to guide strategies for fabricating nanocrystalline materials with improved properties. We report an unusual segregation phenomenon at gold-doped platinum twin boundaries, which is arbitrated by the presence of disconnections, a type of interfacial line defect. By using atomistic simulations, we show that disconnections containing a stacking fault can induce an unexpected transition in the interfacial-segregation structure at the atomic scale, from a bilayer, alternating-segregation structure to a trilayer, segregation-only structure. This behavior is found for faulted disconnections of varying step heights and dislocation characters. Supported by a structural analysis and the classical Langmuir-McLean segregation model, we reveal that this phenomenon is driven by a structurally induced drop of the local pressure across the faulted disconnection accompanied by an increase in the segregation volume. read less N. Zhou, C. Hu, and J. Luo, “Grain Boundary Segregation Transitions and Critical Phenomena in Binary Regular Solutions: A Systematics of Complexion Diagrams with Universal Characters,” Acta Materialia. 2021. link Times cited: 5 J. L. Priedeman and G. Thompson, “The influence of alloying in stabilizing a faceted grain boundary structure,” Acta Materialia. 2020. link Times cited: 11 Z. Pan and F. Sansoz, “Heterogeneous solute segregation suppresses strain localization in nanocrystalline Ag-Ni alloys,” Acta Materialia. 2020. link Times cited: 13 J. Chapman and R. Ramprasad, “Nanoscale Modeling of Surface Phenomena in Aluminum Using Machine Learning Force Fields,” The Journal of Physical Chemistry C. 2020. link Times cited: 7 Abstract: The study of nano-scale surface phenomena is essential in un… read more Abstract: The study of nano-scale surface phenomena is essential in understanding the physical processes that aid in technologically relevant applications, such as catalysis, material growth, and failure nuc... read less P. Cantwell et al., “Grain Boundary Complexion Transitions,” Annual Review of Materials Research. 2020. link Times cited: 94 Abstract: Grain boundaries can undergo phase-like transitions, called … read more Abstract: Grain boundaries can undergo phase-like transitions, called complexion transitions, in which their structure, composition, and properties change discontinuously as temperature, bulk composition, an... read less J. Chapman, R. Batra, and R. Ramprasad, “Machine learning models for the prediction of energy, forces, and stresses for Platinum,” Computational Materials Science. 2020. link Times cited: 18 G. Wang, Y. Xu, P. Qian, and Y. Su, “Vacancy concentration of films and nanoparticles,” Computational Materials Science. 2020. link Times cited: 8 J. Schuler, C. M. Grigorian, C. Barr, B. Boyce, K. Hattar, and T. Rupert, “Amorphous Intergranular Films Mitigate Radiation Damage in Nanocrystalline Cu-Zr,” Nanomaterials eJournal. 2019. link Times cited: 15 Abstract: Nanocrystalline metals are promising radiation tolerant mate… read more Abstract: Nanocrystalline metals are promising radiation tolerant materials due to their large interfacial volume fraction, but irradiation-induced grain growth can eventually degrade any improvement in radiation tolerance. Therefore, methods to limit grain growth and simultaneously improve the radiation tolerance of nanocrystalline metals are needed. Amorphous intergranular films are unique grain boundary structures that are predicted to have improved sink efficiencies due to their increased thickness and amorphous structure, while also improving grain size stability. In this study, ball milled nanocrystalline Cu-Zr alloys are heat treated to either have only ordered grain boundaries or to contain amorphous intergranular films distributed within the grain boundary network, and are then subjected to in situ transmission electron microscopy irradiation and ex situ irradiation. Differences in defect density and grain growth due to grain boundary complexion type are then investigated. When amorphous intergranular films are incorporated within the material, fewer and smaller defect clusters are observed while grain growth is also limited, leading to nanocrystalline alloys with improved radiation tolerance. read less M. Wagih and C. Schuh, “Spectrum of grain boundary segregation energies in a polycrystal,” Acta Materialia. 2019. link Times cited: 74 G. Kaptay, “Thermodynamic Stability of Nano-grained Alloys Against Grain Coarsening and Precipitation of Macroscopic Phases,” Metallurgical and Materials Transactions A. 2019. link Times cited: 12 J. A. Bahena, J. Riano, M. Chellali, T. Boll, and A. Hodge, “Thermally activated microstructural evolution of sputtered nanostructured Mo–Au,” Materialia. 2018. link Times cited: 7 Z. Pan, V. Borovikov, M. Mendelev, and F. Sansoz, “Development of a semi-empirical potential for simulation of Ni solute segregation into grain boundaries in Ag,” Modelling and Simulation in Materials Science and Engineering. 2018. link Times cited: 19 Abstract: An Ag–Ni semi-empirical potential was developed to simulate … read more Abstract: An Ag–Ni semi-empirical potential was developed to simulate the segregation of Ni solutes at Ag grain boundaries (GBs). The potential combines a new Ag potential fitted to correctly reproduce the stable and unstable stacking fault energies in this metal and the existing Ni potential from Mendelev et al (2012 Phil. Mag. 92 4454–69). The Ag–Ni cross potential functions were fitted to ab initio data on the liquid structure of the Ag80Ni20 alloy to properly incorporate the Ag–Ni interaction at small atomic separations, and to the Ni segregation energies at different sites within a high-energy Σ9 (221) symmetric tilt GB. By deploying this potential with hybrid Monte Carlo/molecular dynamics simulations, it was found that heterogeneous segregation and clustering of Ni atoms at GBs and twin boundary defects occur at low Ni concentrations, 1 and 2 at%. This behavior is profoundly different from the homogeneous interfacial dispersion generally observed for the Cu segregation in Ag. A GB transformation to amorphous intergranular films was found to prevail at higher Ni concentrations (10 at%). The developed potential opens new opportunities for studying the selective segregation behavior of Ni solutes in interface-hardened Ag metals and its effect on plasticity. read less T. Frolov, Q. Zhu, T. Oppelstrup, J. Marian, and R. Rudd, “Structures and transitions in bcc tungsten grain boundaries and their role in the absorption of point defects,” Acta Materialia. 2018. link Times cited: 48 I. S. Winter, R. E. Rudd, T. Oppelstrup, and T. Frolov, “Nucleation of Grain Boundary Phases.,” Physical review letters. 2021. link Times cited: 9 Abstract: We derive a theory that describes homogeneous nucleation of … read more Abstract: We derive a theory that describes homogeneous nucleation of grain boundary (GB) phases. Our analysis takes account of the energy resulting from the GB phase junction, the line defect separating two different GB structures, which is necessarily a dislocation as well as an elastic line force due to the jump in GB stresses. The theory provides analytic forms for the elastic interactions and the core energy of the GB phase junction that, along with the change in GB energy, determines the nucleation barrier. We apply the resulting nucleation model to simulations of GB phase transformations in tungsten. Our theory explains why under certain conditions GBs cannot spontaneously change their structure even to a lower energy state. read less X. Zhou et al., “Revealing in-plane grain boundary composition features through machine learning from atom probe tomography data,” Acta Materialia. 2021. link Times cited: 9 C. Li, S. Lu, and L. Vitos, “Predicting grain boundary energies of complex alloys from ab initio calculations,” Scripta Materialia. 2021. link Times cited: 6 T. Frolov, D. Medlin, and M. Asta, “Dislocation content of grain boundary phase junctions and its relation to grain boundary excess properties,” Physical Review B. 2021. link Times cited: 8 Abstract: We analyze the dislocation content of grain boundary (GB) ph… read more Abstract: We analyze the dislocation content of grain boundary (GB) phase junctions, i.e., line defects separating two different GB phases coexisting on the same GB plane. While regular GB disconnections have been characterized for a variety of interfaces, GB phase junctions formed by GBs with different structures and different numbers of excess atoms have not been previously studied. We apply a general Burgers circuit analysis to calculate the Burgers vectors b of junctions in two Σ5 Cu boundaries previously simulated with molecular dynamics. The Burgers vectors of these junctions cannot be described by the displacement shift complete (DSC) lattice alone. We show that, in general, the normal component of b is not equal to the difference in the GB excess volumes, but contains another contribution from the numbers of GB atoms per unit area ∆N∗ required to transform one GB phase into another. In the boundaries studied, the latter component dominates and even changes the sign of b. We derive expressions for the normal and tangential components of b in terms of the DSC lattice vectors and the non-DSC part due to ∆N∗ and additional GB excess properties, including excess volume and shears. These expressions provide a connection between GB phase transformations driven by the GB free energy difference and the motion of GB junctions under applied normal and shear stresses. The proposed analysis quantifies b and therefore makes it possible to calculate the elastic part of the energy of these defects, evaluate their contribution to the nucleation barrier during GB phase transformations, and treat elastic interactions with other defects. read less S. Mathaudhu, “Building on Gleiter: The Foundations and Future of Deformation Processing of Nanocrystalline Metals,” Metallurgical and Materials Transactions A. 2020. link Times cited: 9 N. Peter, M. J. Duarte, C. Kirchlechner, C. Liebscher, and G. Dehm, “Faceting diagram for Ag segregation induced nanofaceting at an asymmetric Cu tilt grain boundary,” arXiv: Materials Science. 2020. link Times cited: 11 X. Zhou, T. Kaub, F. Vogel, and G. Thompson, “Complications of using thin film geometries for nanocrystalline thermal stability investigations,” Journal of Materials Research. 2020. link Times cited: 0 Abstract: We report the sputter deposition of Cu-7V and Cu-27V (at.%) … read more Abstract: We report the sputter deposition of Cu-7V and Cu-27V (at.%) alloy films in an attempt to yield a “clean” alloy to investigate nanocrystalline stability. Films grown in high vacuum chambers can mitigate processing contaminates which convolute the identification of nanocrystalline stability mechanism(s). The initial films were very clean with carbon and oxygen contents ranging between ~0.01 and 0.38 at.%. Annealing at 400 °C/1 h facilitated the clustering of vanadium at high-angle grain boundary triple junctions. At 800 °C/1 h annealing, the Cu-7V film lost its nanocrystalline grain sizes with the vanadium partitioned to the free surface; the Cu-27V retained its nanocrystalline grains with vanadium clusters in the matrix, but surface solute segregation was present. Though the initial alloy and vacuum annealing retained the low contamination levels sought, the high surface area-to-volume ratio of the film, coupled with high segregation tendencies, enabled this system to phase separate in such a manner that the stability mechanisms that were to be studied were lost at high temperatures. This illustrates obstacles in using thin films to address nanocrystalline stability. read less J. Chapman and R. Ramprasad, “Predicting the dynamic behavior of the mechanical properties of platinum with machine learning.,” The Journal of chemical physics. 2020. link Times cited: 2 Abstract: Over the last few decades, computational tools have been ins… read more Abstract: Over the last few decades, computational tools have been instrumental in understanding the behavior of materials at the nano-meter length scale. Until recently, these tools have been dominated by two levels of theory: quantum mechanics (QM) based methods and semi-empirical/classical methods. The former are time-intensive but accurate and versatile, while the latter methods are fast but are significantly limited in veracity, versatility, and transferability. Recently, machine learning (ML) methods have shown the potential to bridge the gap between these two chasms due to their (i) low cost, (ii) accuracy, (iii) transferability, and (iv) ability to be iteratively improved. In this work, we further extend the scope of ML for atomistic simulations by capturing the temperature dependence of the mechanical and structural properties of bulk platinum through molecular dynamics simulations. We compare our results directly with experiments, showcasing that ML methods can be used to accurately capture large-scale materials phenomena that are out of reach of QM calculations. We also compare our predictions with those of a reliable embedded atom method potential. We conclude this work by discussing how ML methods can be used to push the boundaries of nano-scale materials research by bridging the gap between QM and experimental methods. read less E. Antillon and M. Ghazisaeidi, “Efficient determination of solid-state phase equilibrium with the multicell Monte Carlo method.,” Physical review. E. 2020. link Times cited: 5 Abstract: Building on our previously introduced multicell Monte Carlo … read more Abstract: Building on our previously introduced multicell Monte Carlo (MC)^{2} method for modeling phase coexistence, this paper provides important improvements for efficient determination of phase equilibria in solids. The (MC)^{2} method uses multiple cells, representing possible phases. Mass transfer between cells is modeled virtually by solving the mass balance equation after the composition of each cell is changed arbitrarily. However, searching for the minimum free energy during this process poses a practical problem. The solution to the mass balance equation is not unique away from equilibrium, and consequently the algorithm is in risk of getting trapped in nonequilibrium solutions. Therefore, a proper stopping condition for (MC)^{2} is currently lacking. In this work, we introduce a consistency check via a predictor-corrector algorithm to penalize solutions that do not satisfy a necessary condition for equivalence of chemical potentials and steer the system toward finding equilibrium. The most general acceptance criteria for (MC)^{2} is derived starting from the isothermal-isobaric Gibbs ensemble for mixtures. Using this ensemble, translational MC moves are added to include vibrational excitations as well as volume MC moves to ensure the condition of constant pressure and temperature entirely with a MC approach, without relying on any other method for relaxation of these degrees of freedom. As a proof of concept the method is applied to two binary alloys with miscibility gaps and a model quaternary alloy, using classical interatomic potentials. read less J. Hur, “Modified potential for atomistic simulation of the growth of carbon materials from binary alloy catalysts.,” Physical chemistry chemical physics : PCCP. 2019. link Times cited: 3 Abstract: A new hybrid bond order potential has been developed and imp… read more Abstract: A new hybrid bond order potential has been developed and implemented to describe carbon-bimetallic alloy interactions, which are involved in the catalytic growth of carbon materials such as graphene and carbon nanotubes on the surface of binary alloy catalysts. In carefully adjusting the parameters, the potential energy fitting correlated with the results calculated from the density functional theory (DFT) method leads to a high quality empirical force field with an average error of <4.5% only. With the PES accuracy, in total 16 (n,m) have been successfully obtained from the MD trajectories in this work, and the structural evolution including random chirality and diameter formation has been identified. The newly modified force field is expected to be useful for modelling the spontaneous growth of carbon materials, particularly tubes on binary alloy clusters, giving an idea of how these C-C, C-M, and M-M interactions affect the growth behavior of carbon nanotubes. In addition, the new FF is only valid for liquid alloy nanoparticles at this time, but the use of solid alloy nanocatalysts with the new FF can be further employed for 2-D material growth such as graphene layer growth. read less D. Aksoy, R. Dingreville, and D. Spearot, “An embedded-atom method potential parameterized for sulfur-induced embrittlement of nickel,” Modelling and Simulation in Materials Science and Engineering. 2019. link Times cited: 4 Abstract: The embrittling or strengthening effect of solute atoms at g… read more Abstract: The embrittling or strengthening effect of solute atoms at grain boundaries (GBs), commonly known as the embrittling potency, is an essential thermodynamic property for characterizing the effects of solute segregation on GB fracture. One of the more technologically relevant material systems related to embrittlement is the Ni–S system where S has a deleterious effect on fracture behavior in polycrystalline Ni. In this work, we develop a Ni–S embedded-atom method (EAM) interatomic potential that accounts for the embrittling behavior of S at Ni GBs. Results using this new interatomic potential are then compared to previous density functional theory studies and a reactive force-field potential via a layer-by-layer segregation analysis. Our potential shows strong agreement with existing literature and performs well in predicting properties that are not included in the fitting database. Finally, we calculate embrittling potencies and segregation energies for six [100] symmetric-tilt GBs using the new EAM potential. We observe that embrittling potency is dependent on GB structure, indicating that specific GBs are more susceptible to sulfur-induced embrittlement. read less D. Spearot, G. Tucker, A. K. Gupta, and G. Thompson, “Mechanical properties of stabilized nanocrystalline FCC metals,” Journal of Applied Physics. 2019. link Times cited: 13 Abstract: In this perspective, recent advances and current research ch… read more Abstract: In this perspective, recent advances and current research challenges concerning the mechanical properties of stabilized nanocrystalline face-centered cubic (FCC) metals are discussed. First, a brief review of key experiments and modeling efforts over the last two decades is provided, with a focus on elucidating the mechanisms associated with plastic yield, hardening, and microstructure stabilization in nanocrystalline metals. This prior work has provided an understanding of the transition between dislocation-based and grain boundary-mediated mechanisms in plasticity and has identified several strategies to mitigate temperature or stress driven grain growth. Yet, the consequence of various stabilization methods on mechanical properties is not well understood. Future research challenges are presented in order to address this scientific gap, most critically the need to include grain boundary chemistry or grain boundary phases resulting from stabilization methods in new mechanistic theories for mechanical properties of nanocrystalline FCC metals. read less Y. Su, S. Xu, and I. Beyerlein, “Density functional theory calculations of generalized stacking fault energy surfaces for eight face-centered cubic transition metals,” Journal of Applied Physics. 2019. link Times cited: 38 Abstract: In this work, we use density functional theory to calculate … read more Abstract: In this work, we use density functional theory to calculate the entire generalized stacking fault energy (GSFE) surface for eight transition metals with a face-centered cubic structure: Ag, Au, Cu, Ir, Ni, Pd, Pt, and Rh. Analysis of the ⟨ 112 ⟩ GSFE curves finds that the displacements corresponding to the unstable stacking fault energy are larger than the ideal value for all eight metals except Ag and Cu. Over the entire surface, Pt is found to not possess well-defined local maxima or minima, suggesting spreading in favor of dissociation of the dislocation core, unlike the other seven metals. Our calculations also reveal that at a large ⟨ 112 ⟩ displacement, where atoms on two {111} adjacent planes are aligned, an anomalous local minimum occurs for Ir and Rh. The oddity is explained by relatively large, localized atomic displacements that take place in the two metals to accommodate the alignment that do not occur in the other six metals. In addition to the fully calculated surfaces, we characterize a continuous 11-term Fourier-series function, which provides a particularly excellent representation of the GSFE surfaces for Ag, Au, Cu, Ni, and Pd. read less M. Zouini, S. Khamlich, and W. Dimassi, “Bismuth induced crystallization of hydrogenated amorphous silicon thin films: effect of annealing time,” Journal of Materials Science: Materials in Electronics. 2019. link Times cited: 0 N. Peter et al., “Segregation-Induced Nanofaceting Transition at an Asymmetric Tilt Grain Boundary in Copper.,” Physical review letters. 2018. link Times cited: 33 Abstract: We show that chemistry can be used to trigger a nanofaceting… read more Abstract: We show that chemistry can be used to trigger a nanofaceting transition. In particular, the segregation of Ag to an asymmetric tilt grain boundary in Cu is investigated. Aberration-corrected electron microscopy reveals that annealing the bicrystal results in the formation of nanometer-sized facets composed of preferentially Ag-segregated symmetric Σ5{210} segments and Ag-depleted {230}/{100} asymmetric segments. Our observations oppose an anticipated trend to form coarse facets. Atomistic simulations confirm the nanofacet formation observed in the experiment and demonstrate a concurrent grain boundary phase transition induced by the anisotropic segregation of Ag. read less R. D. Kamachali, “A model for grain boundary thermodynamics,” RSC Advances. 2019. link Times cited: 21 Abstract: Systematic microstructure design requires reliable thermodyn… read more Abstract: Systematic microstructure design requires reliable thermodynamic descriptions of each and all microstructure elements. While such descriptions are well established for most bulk phases, thermodynamic assessment of microstructure defects is challenging because of their individualistic nature. In this paper, a model is devised for assessing grain boundary thermodynamics based on available bulk thermodynamic data. We propose a continuous relative atomic density field and its spatial gradients to describe the grain boundary region with reference to the homogeneous bulk and derive the grain boundary Gibbs free energy functional. The grain boundary segregation isotherm and phase diagram are computed for a regular binary solid solution, and qualitatively benchmarked for the Pt–Au system. The relationships between the grain boundary's atomic density, excess free volume, and misorientation angle are discussed. Combining the current density-based model with available bulk thermodynamic databases enables constructing databases, phase diagrams, and segregation isotherms for grain boundaries, opening possibilities for studying and designing heterogeneous microstructures. read less
Funding	Not available

Short KIM ID The unique KIM identifier code.	MO_946831081299_000
Extended KIM ID The long form of the KIM ID including a human readable prefix (100 characters max), two underscores, and the Short KIM ID. Extended KIM IDs can only contain alpha-numeric characters (letters and digits) and underscores and must begin with a letter.	EAM_Dynamo_OBrienBarrPrice_2018_PtAu__MO_946831081299_000
DOI	10.25950/a57b36d8 https://doi.org/10.25950/a57b36d8 https://commons.datacite.org/doi.org/10.25950/a57b36d8
KIM Item Type Specifies whether this is a Portable Model (software implementation of an interatomic model); Portable Model with parameter file (parameter file to be read in by a Model Driver); Model Driver (software implementation of an interatomic model that reads in parameters).	Portable Model using Model Driver EAM_Dynamo__MD_120291908751_005
Driver	EAM_Dynamo__MD_120291908751_005
KIM API Version	2.0
Potential Type	eam
Forked By	EAM_QuinticClampedSpline_Kim_2021_PtAu__MO_463728687265_000

Verification Check Dashboard

(Click here to learn more about Verification Checks)

Grade	Name	Category	Brief Description	Full Results	Aux File(s)
P All elements claimed by model are supported in at least one of the tested configurations.	vc-species-supported-as-stated	mandatory	The model supports all species it claims to support; see full description.	Results	Files
P Periodic boundary conditions were correctly supported for all configurations that the model was able to compute.	vc-periodicity-support	mandatory	Periodic boundary conditions are handled correctly; see full description.	Results	Files
P Model energy has permutation symmetry for all configurations the model was able to compute.	vc-permutation-symmetry	mandatory	Total energy and forces are unchanged when swapping atoms of the same species; see full description.	Results	Files
C The letter grade C was assigned because the normalized error in the computation was 8.08896e-06 compared with a machine precision of 2.22045e-16. The letter grade was based on 'score=log10(error/eps)', with ranges A=[0, 7.5], B=(7.5, 10.0], C=(10.0, 12.5], D=(12.5, 15.0), F>15.0. 'A' is the best grade, and 'F' indicates failure.	vc-forces-numerical-derivative	consistency	Forces computed by the model agree with numerical derivatives of the energy; see full description.	Results	Files
F The model is C^0 continuous. This means that the model has continuous energy, but a discontinuous first derivative.	vc-dimer-continuity-c1	informational	The energy versus separation relation of a pair of atoms is C1 continuous (i.e. the function and its first derivative are continuous); see full description.	Results	Files
P Model energy and forces are invariant with respect to rigid-body motion (translation and rotation) for all configurations the model was able to compute.	vc-objectivity	informational	Total energy is unchanged and forces transform correctly under rigid-body translation and rotation; see full description.	Results	Files
P Model energy has inversion symmetry for all configurations the model was able to compute.	vc-inversion-symmetry	informational	Total energy is unchanged and forces change sign when inverting a configuration through the origin; see full description.	Results	Files
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: Au

Species: Pt

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: Pt

Species: Au

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: Pt

Species: Au

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.

Species: Pt

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: Au

Species: Pt

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: Pt

Species: Au

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: Au

Species: Pt

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: Au

Species: Pt

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: Pt

Species: Au

Cubic Crystal Basic Properties Table

Species: Au

Species: Pt

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 Au v004	view	8498
Cohesive energy versus lattice constant curve for bcc Pt v004	view	5799
Cohesive energy versus lattice constant curve for diamond Au v004	view	7241
Cohesive energy versus lattice constant curve for diamond Pt v004	view	6773
Cohesive energy versus lattice constant curve for fcc Au v004	view	6714
Cohesive energy versus lattice constant curve for fcc Pt v004	view	5679
Cohesive energy versus lattice constant curve for sc Au v004	view	6783
Cohesive energy versus lattice constant curve for sc Pt v004	view	6847

DislocationCoreEnergyCubic__TD_452950666597_002

Dislocation core energy for cubic crystals at a set of dislocation core cutoff radii v002

Creators:
Contributor: qyc081025
Publication Year: 2023
DOI: https://doi.org/10.25950/ebecf626

This Test Driver computes the dislocation core energy of a cubic crystal at zero temperature and pressure for a specific set of dislocation core cutoff radii. First, it generates several periodic atomistic supercells containing a dislocation dipole. 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. The supercell is increased in size until the disolcation core energy converges. Finally, after checking the independence of the results from the simulation cell geometry, the dislocation core energies are determined for each dislocation core radius.

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)
Dislocation core energy for fcc Pt computed at zero temperature for a set of dislocation core cutoff radii with burgers vector [0.5, 0.5, 0] along line direction [1, 1, 0] v000		view	12593375

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 Au at zero temperature v006	view	6526
Elastic constants for bcc Pt at zero temperature v006	view	1983
Elastic constants for fcc Au at zero temperature v006	view	1440
Elastic constants for fcc Pt at zero temperature v006	view	4159
Elastic constants for sc Au at zero temperature v006	view	2047
Elastic constants for sc Pt at zero temperature v006	view	4255

ElasticConstantsHexagonal__TD_612503193866_004

Elastic constants for hexagonal crystals at zero temperature v004

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

Computes the elastic constants for hcp crystals by calculating the hessian of the energy density with respect to strain. An estimate of the error associated with the numerical differentiation performed is reported.

Test	Test Results	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Elastic constants for hcp Au at zero temperature v004		view	1433
Elastic constants for hcp Pt at zero temperature v004		view	1783

EquilibriumCrystalStructure__TD_457028483760_001

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

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

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

Test	Test Results	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Equilibrium crystal structure and energy for Au in AFLOW crystal prototype A_cF4_225_a v001		view	84884

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	Test Results	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Equilibrium crystal structure and energy for Pt in AFLOW crystal prototype A_cF4_225_a v002		view	65195

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 111 symmetric tilt grain boundary in fcc Au v000		view	8949308
Relaxed energy as a function of tilt angle for a 112 symmetric tilt grain boundary in fcc Au v000		view	35466381

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 Au v007	view	1887
Equilibrium zero-temperature lattice constant for bcc Pt v007	view	1951
Equilibrium zero-temperature lattice constant for diamond Au v007	view	2527
Equilibrium zero-temperature lattice constant for diamond Pt v007	view	2911
Equilibrium zero-temperature lattice constant for fcc Au v007	view	3263
Equilibrium zero-temperature lattice constant for fcc Pt v007	view	2559
Equilibrium zero-temperature lattice constant for sc Au v007	view	2047
Equilibrium zero-temperature lattice constant for sc Pt v007	view	2687

LatticeConstantHexagonalEnergy__TD_942334626465_005

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

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

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

Test	Test Results	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Equilibrium lattice constants for hcp Au v005		view	15122
Equilibrium lattice constants for hcp Pt v005		view	15154

LinearThermalExpansionCoeffCubic__TD_522633393614_001

Linear thermal expansion coefficient of cubic crystal structures v001

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

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

Test	Test Results	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Linear thermal expansion coefficient of fcc Au at 293.15 K under a pressure of 0 MPa v001		view	4401590

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 Pt at 293.15 K under a pressure of 0 MPa v002		view	635271

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 Au v004		view	51918
Phonon dispersion relations for fcc Pt v004		view	45104

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 Au v002		view	8254154
Stacking and twinning fault energies for fcc Pt v002		view	4935103

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 Au v004		view	30965
Broken-bond fit of high-symmetry surface energies in fcc Pt v004		view	25591

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 Au		view	316715
Monovacancy formation energy and relaxation volume for fcc Pt		view	383342

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 Au		view	2802880
Vacancy formation and migration energy for fcc Pt		view	1545736

Errors

ElasticConstantsCubic__TD_011862047401_006

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

GrainBoundaryCubicCrystalSymmetricTiltRelaxedEnergyVsAngle__TD_410381120771_003

Test	Error Categories	Link to Error page
Relaxed energy as a function of tilt angle for a 100 symmetric tilt grain boundary in fcc Au v000	other	view
Relaxed energy as a function of tilt angle for a 100 symmetric tilt grain boundary in fcc Pt v000	other	view
Relaxed energy as a function of tilt angle for a 110 symmetric tilt grain boundary in fcc Au v000	other	view
Relaxed energy as a function of tilt angle for a 110 symmetric tilt grain boundary in fcc Pt v000	other	view
Relaxed energy as a function of tilt angle for a 111 symmetric tilt grain boundary in fcc Pt v000	other	view
Relaxed energy as a function of tilt angle for a 112 symmetric tilt grain boundary in fcc Pt v000	other	view

LatticeConstantCubicEnergy__TD_475411767977_006

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

LatticeConstantHexagonalEnergy__TD_942334626465_004

Test	Error Categories	Link to Error page
Equilibrium lattice constants for hcp Au	other	view
Equilibrium lattice constants for hcp Pt	other	view

Files

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

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EAM_Dynamo_OBrienBarrPrice_2018_PtAu__MO_946831081299_000.txz	Tar+XZ	Linux and OS X archive
EAM_Dynamo_OBrienBarrPrice_2018_PtAu__MO_946831081299_000.zip	Zip	Windows archive

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

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

Wiki

## 1. Inconsistency in parameters

Line 5 of an EAM file in setfl format lists Nrho, drho, Nr, dr, cutoff, where Nrho is the number of tabulated values for embedding function and Nr is the number of tabulated values for electron density function and pair energy function in the subsequent arrays, drho is the spacing in electron density for embedding energy and dr is the distance space for electron density function and pair energy function, and cutoff is the global cutoff radius for both electron density function and pair energy function. The maximum electron density for embedding energy, rhomax, is 50 [1]. These variables should meet the following relations:

$$
drho = \frac{rhomax}{Nrho - 1}
$$

and

$$
dr = \frac{cutoff}{Nr - 1}
$$

However, the values in the parameter file (PtAu.eam.alloy), which are

5000 1.000000000000e-02 5000 1.150000000000e-03 5.750000000000,

do not satisfy these relations.

## 2. Discontinuity issue

The model does not pass the continuity test (vc-dimer-continuity-c1) because the electron density and pair energy functions change too abruptly toward the cutoff radius.

## References

[1] O’Brien CJ, Barr CM, Price PM, Hattar K, Foiles SM. Grain boundary phase transformations in PtAu and relevance to thermal stabilization of bulk nanocrystalline metals, Journal of Materials Science, 53, 2911–2927, 2018.

Wiki Contributors

2021-01-11T13:08:53.346116	kwk4814

EAM_Dynamo_OBrienBarrPrice_2018_PtAu__MO_946831081299_000

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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

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