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At least 19 records

CuAu binary alloy with 32 atoms - LSMS-3 data

This dataset contains the estimate of atomic charge density, atomic magnetic moment and total energy for 32,000 configurations of the iron-platinum (CuAu) binary alloy with face-centered cubic (FCC) structure. The configurations span all the compositions from 0%Cu - 100% Au through 100%Cu - 0% Au. The results have been produced running ab-initio density functional theory (DFT) calculations with the LSMS-3 code on OLCF supercomputer Titan. LSMS-3 GitHub repository: https://github.com/mstsuite/lsms Deep Learning research papers published with results based on this dataset: Fast and stable deep-learning predictions of material properties for solid solution alloys Massimiliano Lupo Pasini, Ying Wai Li, Junqi Yin, Jiaxin Zhang, Kipton Barros and Markus Eisenbach Published 14 December 2020. 2020 IOP Publishing Ltd Journal of Physics: Condensed Matter, Volume 33, Number 8 Citation Massimiliano Lupo Pasini et al 2021 J. Phys.: Condens. Matter 33 084005 https://iopscience.iop.org/article/10.1088/1361-648X/abcb10

36 MATERIALS SCIENCE↗

Ordering in CuAu

Rapidly quenched CuAu ordered state development on low temperature annealing, presenting Young modulus variation as function of heat treatment time

Brotzen, F. R.↗

Materials Data on CuAu by Materials Project

AuCu is Tetraauricupride structured and crystallizes in the tetragonal P4/mmm space group. The structure is three-dimensional. Au1- is bonded in a distorted body-centered cubic geometry to eight equivalent Cu1+ atoms. All Au–Cu bond lengths are 2.73 Å. Cu1+ is bonded in a body-centered cubic geometry to eight equivalent Au1- atoms.

36 MATERIALS SCIENCE↗

Materials Data on CuAu by Materials Project

AuCu crystallizes in the triclinic P-1 space group. The structure is three-dimensional. there are five inequivalent Au1- sites. In the first Au1- site, Au1- is bonded in a 12-coordinate geometry to four Au1- and eight Cu1+ atoms. There are two shorter (2.82 Å) and two longer (2.84 Å) Au–Au bond lengths. There are a spread of Au–Cu bond distances ranging from 2.70–2.86 Å. In the second Au1- site, Au1- is bonded in a distorted body-centered cubic geometry to two equivalent Au1- and eight Cu1+ atoms. There are a spread of Au–Cu bond distances ranging from 2.72–2.80 Å. In the third Au1- site, Au1- is bonded in a distorted body-centered cubic geometry to eight Cu1+ atoms. There are a spread of Au–Cu bond distances ranging from 2.72–2.74 Å. In the fourth Au1- site, Au1- is bonded in a distorted body-centered cubic geometry to eight Cu1+ atoms. There are two shorter (2.72 Å) and six longer (2.73 Å) Au–Cu bond lengths. In the fifth Au1- site, Au1- is bonded in a distorted body-centered cubic geometry to eight Cu1+ atoms. There are two shorter (2.72 Å) and six longer (2.73 Å) Au–Cu bond lengths. There are five inequivalent Cu1+ sites. In the first Cu1+ site, Cu1+ is bonded in a 8-coordinate geometry to eight Au1- atoms. In the second Cu1+ site, Cu1+ is bonded in a body-centered cubic geometry to eight Au1- atoms. In the third Cu1+ site, Cu1+ is bonded in a body-centered cubic geometry to eight Au1- atoms. In the fourth Cu1+ site, Cu1+ is bonded in a body-centered cubic geometry to eight Au1- atoms. In the fifth Cu1+ site, Cu1+ is bonded in a body-centered cubic geometry to eight Au1- atoms.

36 MATERIALS SCIENCE↗

Revealing the Predominant Surface Facets of Rough Cu Electrodes under Electrochemical Conditions

Metal electrodes with rough surfaces are frequently found to convert CO or CO 2 to hydrocarbons and oxygenates with high selectivity and at high reaction rates in comparison with their smooth counterparts. The atomic-level morphology of a rough electrode is likely one key factor responsible for its comparatively high catalytic selectivity and activity. However, few methods are capable of probing the atomic-level structure of rough metal electrodes under electrocatalytic conditions. As a result, the nuances in the atomic-level surface morphology that control the catalytic characteristics of these electrodes have remained largely unexplored. Because the C≡O stretching frequency of atop-bound CO (CO atop ) depends on the coordination of the underlying metal atom, the IR spectrum of this reaction intermediate on the copper electrode could, in principle, provide structural information about the catalytic surface during electrolysis. However, other effects, such as dynamic dipole coupling, easily obscure the dependence of the frequency on the surface morphology. Further, in the limit of low CO atop coverage, where coupling effects are small, the C≡O stretching frequencies of CO atop on Cu(111) and Cu(100) facets are virtually identical. Therefore, on the basis of the C≡O stretching frequency, it is not straightforward to distinguish between these two ubiquitous surface facets, which exhibit vastly different CO reduction activities. Herein, we show that key features of the atomic-level surface morphology of rough copper electrodes can be inferred from the potential dependence of the line shape of the C≡O stretching band of CO atop . Specifically, we compared two types of rough copper thin-film electrodes that are routinely employed in the context of surface-enhanced infrared absorption spectroscopy (SEIRAS). We found that copper films that are electrochemically deposited on Si-supported Au films (CuAu–Si) are poor catalysts for the reduction of CO to ethylene in comparison to copper films (Cu–Si) that are electrolessly deposited onto Si crystals. As quantified by differential electrochemical mass spectrometry (DEMS), the onset potential for ethylene is ~200 ± 65 mV more cathodic for CuAu–Si than that for Cu–Si. To reveal the origin of the disparate catalytic properties of Cu–Si and CuAu–Si, we probed the surfaces of the electrodes with cyclic voltammetry (CV) and SEIRAS. The CV characterization suggests that the (111) surface facet predominates on CuAu–Si, whereas the (100) facet is more common on Cu–Si. SEIRAS reveals that the line shape of the C≡O stretching of CO atop is composed of two bands that are attributable to CO atop on terrace and defect sites. The different surface structures manifest themselves in the form of starkly different potential dependences of the line shape of the C≡O stretching mode of CO atop on the two types of electrodes. With a simple Boltzmann model that considers the different adsorption energies of CO atop on terrace and defect sites, and the resulting CO atop populations on terrace and defect sites, we deduced that the observed electrode-specific potential dependence of the line shape is consistent with the presence of different predominant terrace sites on the two types of films. This strategy for assessing the atomic-level morphology is not restricted to SEIRAS but could also be applied to the C≡O stretching bands recorded with surface-enhanced Raman spectroscopy (SERS), which is suitable for probing a wide range of rough copper electrodes. Therefore, with this work, we establish the potential dependence of the C≡O stretching band of CO atop as a probe of the atomic-level surface structure of rough metal electrodes under electrochemical conditions. When it is coupled with complementary techniques, this methodology provides essential structural information for further improvement in the reaction selectivity of rough metal electrodes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Tuning Strong Metal–Support Interactions via Synergistic Alloying

The encapsulation phenomenon associated with strong metal-support interaction (SMSI) has been largely restricted to catalyst systems consisting of group VIII metals with high surface energy and reducible transition metal oxide supports with low surface energy. Here, we demonstrate an encapsulation phenomenon that, while sharing morphological similarities with conventional SMSI, follows a distinctive pathway. This is shown by the encapsulation of CuAu nanoparticles (NPs) supported on highly ordered pyrolytic graphite (HOPG). Through dynamic monitoring of Cu, Au, and Cu 50 Au 50 NPs in an oxidizing atmosphere using ambient-pressure X-ray photoelectron spectroscopy, we show that this spontaneous encapsulation is achieved through the synergistic effect of the alloying elements. Specifically, the surface segregation of Cu promotes dissociative O 2 adsorption, leading to the formation of atomic O species, while the subsurface enrichment of Au hinders O incorporation into the bulk of CuAu NPs. Consequently, O spillover onto the graphite support occurs, resulting in the oxidation of the HOPG surface into graphitic oxide species. The higher affinity of the graphitic oxide species toward the Cu-segregated surface prompts their migration from the HOPG support to encapsulate the CuAu NPs. Finally, these results transcend the conventional SMSI and bear practical implications for the design and development of heterogeneous catalysts, particularly in carbon-supported alloy systems.

36 MATERIALS SCIENCE↗

Field-ion microscopy of ordered Cu-Au alloy.

A method is proposed for overcoming current difficulties in measuring atomic order parameters of nonrefractory metals when using the field-ion microscope (FIM). Near stoichiometric CuAu and Cu3Au were tested by means of this method. Images of substantially fully ordered CuAu and Cu3Au thus obtained are presented and discussed.

Son, U. T.↗

Cu-Au Alloys Using Monte Carlo Simulations and the BFS Method for Alloys

Semi empirical methods have shown considerable promise in aiding in the calculation of many properties of materials. Materials used in engineering applications have defects that occur for various reasons including processing. In this work we present the first application of the BFS method for alloys to describe some aspects of microstructure due to processing for the Cu-Au system (Cu-Au, CuAu3, and Cu3Au). We use finite temperature Monte Carlo calculations, in order to show the influence of 'heat treatment' in the low-temperature phase of the alloy. Although relatively simple, it has enough features that could be used as a first test of the reliability of the technique. The main questions to be answered in this work relate to the existence of low temperature ordered structures for specific concentrations, for example, the ability to distinguish between rather similar phases for equiatomic alloys (CuAu I and CuAu II, the latter characterized by an antiphase boundary separating two identical phases).

Bozzolo, Guillermo↗

Calculated X-ray Intensities Using Monte Carlo Algorithms: A Comparison to Experimental EPMA Data

Monte Carlo (MC) modeling has been used extensively to simulate electron scattering and x-ray emission from complex geometries. Here are presented comparisons between MC results and experimental electron-probe microanalysis (EPMA) measurements as well as phi(rhoz) correction algorithms. Experimental EPMA measurements made on NIST SRM 481 (AgAu) and 482 (CuAu) alloys, at a range of accelerating potential and instrument take-off angles, represent a formal microanalysis data set that has been widely used to develop phi(rhoz) correction algorithms. X-ray intensity data produced by MC simulations represents an independent test of both experimental and phi(rhoz) correction algorithms. The alpha-factor method has previously been used to evaluate systematic errors in the analysis of semiconductor and silicate minerals, and is used here to compare the accuracy of experimental and MC-calculated x-ray data. X-ray intensities calculated by MC are used to generate a-factors using the certificated compositions in the CuAu binary relative to pure Cu and Au standards. MC simulations are obtained using the NIST, WinCasino, and WinXray algorithms; derived x-ray intensities have a built-in atomic number correction, and are further corrected for absorption and characteristic fluorescence using the PAP phi(rhoz) correction algorithm. The Penelope code additionally simulates both characteristic and continuum x-ray fluorescence and thus requires no further correction for use in calculating alpha-factors.

Carpenter, P. K.↗

Fast and Accurate Predictions of Total Energy for Solid Solution Alloys with Graph Convolutional Neural Networks

We use graph convolutional neural networks (GCNNs) to produce fast and accurate predictions of the total energy of solid solution binary alloys. GCNNs allow us to abstract the lattice structure of a solid material as a graph, whereby atoms are modeled as nodes and metallic bonds as edges. This representation naturally incorporates information about the structure of the material, thereby eliminating the need for computationally expensive data pre-processing which would be required with standard neural network (NN) approaches. We train GCNNs on ab-initio density functional theory (DFT) for copper-gold (CuAu) and iron-platinum (FePt) data that has been generated by running the LSMS-3 code, which implements a locally self-consistent multiple scattering method, on OLCF supercomputers Titan and Summit. GCNN outperforms the ab-initio DFT simulation by orders of magnitude in terms of computational time to produce the estimate of the total energy for a given atomic configuration of the lattice structure. We compare the predictive performance of GCNN models against a standard NN such as dense feedforward multi-layer perceptron (MLP) by using the root-mean-squared errors to quantify the predictive quality of the deep learning (DL) models. We find that the attainable accuracy of GCNNs is at least an order of magnitude better than that of the MLP.

Lupo Pasini, Massimiliano↗

Synthesis and characterization of Pt(Cu 0.67 Sn 0.33 )

Pt(Cu 0.67 Sn 0.33 ) has recently been found in a natural sample. In order to be able to characterize this new ternary compound, we synthesized it from the elements. Samples were characterized by X-ray powder diffraction, differential scanning calorimetry, thermal relaxation calorimetry, and scanning electron microscopy studies. Density functional theory-based model calculations complemented the experimental studies. Pt(Cu 0.67 Sn 0.33 )was already formed at a relatively low temperature of 773 K. Rietveld refinement of Pt(Cu 0.67 Sn 0.33 ) has been carried out in CuAu-type or L1 0 -type structure, space group P4/mmm, with Pt on 0,0,0 and disordered Cu and Sn on 1/2, 1/2, 1/2 and Z = 1. The lattice parameters are a = 2.823(1) Å, c = 3.64(1) Å, and V = 29.00(4) Å which are in good agreement with values obtained earlier on the natural sample and with the results of DFT calculations. The vibrational entropy for Pt(Cu 0.67 Sn 0.33 ) is $S_{298.15}^{vib}$ = 79.9(7) J mol -1 K -1 . The pressure dependence up to 36(2) GPa of the unit-cell volume and the lattice parameters and unit-cell volume have been obtained by synchrotron based powder diffraction using a diamond anvil cell. A fit of a 3rd-order Birch–Murnaghan equation of state to the Pt(Cu 0.67 Sn 0.33 )) (p,V)-data results in a bulk modulus of B 0 = 215(27) GPa and B' = 5(2).

36 MATERIALS SCIENCE↗

Sampling lattices in semi-grand canonical ensemble with autoregressive machine learning

Calculating thermodynamic potentials and observables efficiently and accurately is key for the application of statistical mechanics simulations to materials science. However, naive Monte Carlo approaches, on which such calculations are often dependent, struggle to scale to complex materials in many state-of-the-art disciplines such as the design of high entropy alloys or multi-component catalysts. To address this issue, we adapt sampling tools built upon machine learning-based generative modeling to the materials space by transforming them into the semi-grand canonical ensemble. Furthermore, we show that the resulting models are transferable across wide ranges of thermodynamic conditions and can be implemented with any internal energy model U, allowing integration into many existing materials workflows. We demonstrate the applicability of this approach to the simulation of benchmark systems (AgPd, CuAu) that exhibit diverse thermodynamic behavior in their phase diagrams. Finally, we discuss remaining challenges in model development and promising research directions for future improvements.

36 MATERIALS SCIENCE↗

Ab-initio Cu alloy design for high-gradient accelerating structures

Operation of normal conducting accelerator structures at high accelerating gradients is beneficial for many accelerator applications in basic science, industry, medicine, and National Security. RF breakdown is the major factor that limits the achievable accelerating gradients. Previous experiments on copper (Cu) have demonstrated that RF breakdown probability can be significantly decreased by hardening the material and alloying Cu with solutes such as silver (Ag). In this paper, we propose a figure-of-merit (FOM) that characterizes the ability of Cu alloys to withstand high-gradients. The FOM represents a trade-off between hardening through solid solution strengthening and the additional thermal stress induced by incremental RF pulse heating resulting from changes in electronic properties induced by alloying. We performed high-throughput ab initio calculations and computed the FOM for a large number of binary Cu alloys. Several promising candidate alloys for high-gradient accelerating structures were identified, such as CuAg, CuCd, CuHg, CuAu, CuIn, and CuMg. CuAg alloys have previously exhibited low RF breakdown rates in experiments. The results provide guidance for selecting alloys for the future high-gradient normal conducting accelerating structures operating at very high gradients.

36 MATERIALS SCIENCE↗

Regulating phase behavior of nanoparticle assemblies through engineering of DNA-mediated isotropic interactions

Self-assembly of isotropically interacting particles into desired crystal structures could allow for creating designed functional materials via simple synthetic means. However, the ability to use isotropic particles to assemble different crystal types remains challenging, especially for generating low-coordinated crystal structures. Here, we demonstrate that isotropic pairwise interparticle interactions can be rationally tuned through the design of DNA shells in a range that allows transition from common, high-coordinated FCC-CuAu and BCC-CsCl lattices, to more exotic symmetries for spherical particles such as the SC-NaCl lattice and to low-coordinated crystal structures (i.e., cubic diamond, open honeycomb). The combination of computational and experimental approaches reveals such a design strategy using DNA-functionalized nanoparticles and successfully demonstrates the realization of BCC-CsCl, SC-NaCl, and a weakly ordered cubic diamond phase. The study reveals the phase behavior of isotropic nanoparticles for DNA–shell tunable interaction, which, due to the ease of synthesis is promising for the practical realization of non-close-packed lattices.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Fast and stable deep-learning predictions of material properties for solid solution alloys

We present a novel deep learning (DL) approach to produce highly accurate predictions of macroscopic physical properties of solid solution binary alloys and magnetic systems. The major idea is to make use of the correlations between different physical properties in alloy systems to improve the prediction accuracy of neural network (NN) models. We use multitasking NN models to simultaneously predict the total energy, charge density and magnetic moment. These physical properties mutually serve as constraints during the training of the multitasking NN, resulting in more reliable DL models because multiple physics properties are correctly learned by a single model. Two binary alloys, copper–gold (CuAu) and iron–platinum (FePt), were studied. Our results show that once the multitasking NN's are trained, they can estimate the material properties for a specific configuration hundreds of times faster than first-principles density functional theory calculations while retaining comparable accuracy. We used a simple measure based on the root-mean-squared errors to quantify the quality of the NN models, and found that the inclusion of charge density and magnetic moment as physical constraints leads to more stable models that exhibit improved accuracy and reduced uncertainty for the energy predictions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Assessment of AFM - KPFM and SSRM for Measuring and Characterizing Materials Aging Processes

Atomic Force Microscopy (AFM), in conjunction with Peak Force Kelvin Probe Force Microscopy (PF-KPFM) and Peak Force Scanning Spreading Resistance Microscopy (PF-SSRM), was used to assess changes on thin metal films that underwent accelerated aging. The AFM technique provides a relatively easy, non-destructive methodology that does not require high-vacuum facilities to obtain nanometer-scale spatial resolution of surface chemistry changes. Surface morphology, roughness, contact potential difference, and spreading resistance were monitored to qualitatively identify effects of aging-morphology changes and oxidation of Au, Al, Cu thin film standards as well as diffusion of CuAu and AlAu thin film stacks at 65C under dried nitrogen flow conditions. AFM PF-KPFM and PF-SSRM modes have been exercised, refined and have proven to be viable and necessary early aging detection tools.

36 MATERIALS SCIENCE↗