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Results for “crystal structure prediction”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A crystal plasticity model with an atomistically informed description of grain boundary sliding for improved predictions of deformation fields

Crystal plasticity (CP) is a powerful meso-scale technique for deformation modeling in polycrystalline materials. Crystal plasticity models typically do not include an explicit description of grain boundary (GB) sliding, which could lead to inaccurate predictions of strain distributions, especially in the vicinity of GBs. In the present study, a CP model is developed that includes GB sliding as an additional deformation mechanism and models the interaction between slip and GB sliding. Atomistic simulations are used to formulate the constitutive model for GB sliding and its interaction with incident slip. The deformation fields and structure of the GBs are obtained directly from experimental characterization and are faithfully reproduced as bicrystal systems for molecular dynamics simulations. The GBs are modeled as random-type (non-coincidence, asymmetrical) boundaries as observed from experimental data. The underlying atomistic-scale mechanism for pure sliding of random GBs was found to be analogous to fluid-flow. The interaction between incident slip and a sliding GB caused local increases in stress concentration, which further led to a momentary increase in the local sliding rate. The displacement profiles at the sliding GBs computed from the CP model with sliding accommodation are 38% more accurate than the baseline CP model as quantified by the mean squared error between the simulations and experiment. Here these results help improve the sophistication and accuracy of deformation modeling by including physics-based descriptions for GB sliding and its interaction with slip, eventually leading to more reliable predictions of micromechanical quantities.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Nanostructures evolution assessment and spectroscopic properties modification induced by electronic energy loss in KTaO 3 crystal

Modifications of micro/nanostructure and photoelectric properties under swift ion irradiation (645 MeV Xe 35+ ) of KTaO 3 crystal with varying electronic energy losses (7.2–31.4 keV/nm) and ion velocities (0.18–5.00 MeV/u) have been studied by combining experimental and calculated approaches. The i-TS calculations combined with molecular dynamics simulations are compared with the experimental observations, revealing the inner track fine structures from individual spherical defects to continuous ion tracks with core–shell morphologies, and a quantitative relationship, including the melting (0.42 eV/atom), damage (0.75 eV/atom), and amorphous (1.71 eV/atom) thresholds, is established to successfully predict the inner disorder/amorphous proportions and track damage morphologies. The surface nanostructures of nanohillocks (isolated or partial overlaps) and nanopits (serious overlaps) directly depend on the combined action of the deposited potential and kinetic energies of incident ions, which induce local melting and sublimation in the near-surface region. Owing to the decreased recombination and the increased separation efficiency between electrons and holes, the higher photogenerated charge carrier mobility enhanced the photocurrent effect, further optimizing the photoconductivity performance in Xe 35+ -irradiated KTaO 3 . Therefore, controlled defect engineering using the ion irradiation technique, as an effective strategy, could design tailored nanostructure systems and regulate photoelectric properties, further promoting the development of novel technological applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A machine-learning approach to measure 3D sample properties from 2D Transmission Electron Microscopy images

Transmission Electron Microscopy (TEM) is a powerful tool for the characterization of materials at the nanoscale; however, its inherent two-dimensional (2D) nature poses significant challenges to accurately measure three-dimensional (3D) properties. We introduce a supervised machine-learning model that predicts 3D structural information, such as sample thickness and curvature, from a series of conventional 2D TEM images. The model, a U-Net convolutional neural network, is trained on a large synthetic dataset generated from dynamical diffraction simulations that model TEM’s complex, nonlinear image formation, accounting for sample thickness and curvature. This physically realistic framework enables exploration of a broad parameter space impractical to sample experimentally. We demonstrate that the trained model has accurate predictions for experimental single-crystal silicon samples, achieving performance comparable to established measurement techniques. This work highlights the critical role of robust, simulation-based training in overcoming the limitations of real-world imaging artifacts and inconsistent sample geometries. By integrating machine learning with numerical simulations, we offer an efficient and scalable framework for quantitative TEM analysis, paving the way for more sophisticated 3D characterization of complex materials.

Dynamical diffraction↗
DOE OSTI2021

Crystalline symmetry-protected non-trivial topology in prototype compound BaAl 4

The BaAl 4 prototype crystal structure is the most populous of all structure types, and is the building block for a diverse set of sub-structures including the famous ThCr 2 Si 2 family that hosts high-temperature superconductivity and numerous magnetic and strongly correlated electron systems. The MA 4 family of materials (M = Sr, Ba, Eu; A = Al, Ga, In) themselves present an intriguing set of ground states including charge and spin orders, but have largely been considered as uninteresting metals. We predict the exemplary compound BaAl 4 to harbor a three-dimensional Dirac spectrum with non-trivial topology and possible nodal lines crossing the Brillouin zone, wherein one pair of semi-Dirac points with linear dispersion along the k z direction and quadratic dispersion along the k x / k y direction resides on the rotational axis with C 4 v point group symmetry. An extremely large, unsaturating positive magnetoresistance in BaAl 4 despite an uncompensated band structure is revealed, and quantum oscillations and angle-resolved photoemission spectroscopy measurements confirm the predicted multiband semimetal structure with pockets of Dirac holes and a Van Hove singularity (VHS) remarkably consistent with the theoretical prediction. We thus present BaAl 4 as a topological semimetal, casting its prototype status into a role as a building block for a vast array of topological materials.

36 MATERIALS SCIENCE↗
NASA NTRS2020

Evidence for a Stratified Upper Mantle Preserved within the South Pole - Aitken Basin

The evolution and compositional structure of the lunar mantle has been extensively modeled but insufficiently constrained by observations. Here, we identify and characterize mantle materials exposed by the Moon's largest impact basin to better understand the composition, stratigraphy, and evolution of the upper mantle. The vast South Pole-Aitken Basin (SPA) exhibits a broad, crescent-shaped thorium and potassium distribution. These incompatible elements are predicted to be concentrated in the dregs of the lunar magma ocean during end-stage crystallization. Through consideration of basin formation models convolved with subsequent geologic evolution, we demonstrate that the distribution and implied stratigraphy of Th- and K-bearing materials across SPA are consistent with an upper mantle ejecta origin. The most pristine exposures of these materials are confined to northwest SPA and also exhibit elevated Ti and Fe (relative to the farside highlands) in association with a gabbronoritic mineralogy. This is consistent with latestage magma ocean assemblages predicted by petrologic models. In contrast, SPA impact melt derived from greater depths is associated with a low-Ca pyroxene-dominated assemblage. Together, these compositional patterns are evidence for a stratified ancient upper mantle. Importantly, the incompatible-element-enriched, ilmenite-bearing ferroan gabbronoritic cumulates evidently had not participated in gravitational overturn at the time of SPA formation. Contrary to recent hypotheses invoking nearside sequestration of incompatible elements to explain hemispherical differences in crustal building and volcanic resurfacing, it follows that incompatible elements were globally distributed in the magma ocean at the time of SPA formation.

Lunar dichotomy↗
DOE OSTI2022

Sensitivity Study of Multiscale and Phenomenological Elasto-Viscoplastic Grade 91 Material Models for Component-Scale Response

Many advanced nuclear reactor concepts currently being developed are targeting higher operating temperatures relative to the current fleet of light water nuclear reactors, for efficiency gains and other operational considerations. The design of high temperature structural components with reliable long-term operational performance will depend on material models that accurately capture the inelastic deformation mechanisms active in these environments. In this work, we perform a detailed parameter sensitivity analysis of two unified elasto-viscoplastic Grade 91 material models capable of capturing long term high temperature creep deformation. The first model is a phenomelogical material model from the Nuclear Engineering Material Library (NEML) developed at Argonne National Lab. The NEML model parameters and their uncertainty were fit to a range of Grade 91 experimental data using Bayesian Markov Chain Monte Carlo analysis. The second model is a LAROMance data-driven surrogate material model developed at Los Alamos National Lab. The LAROMance model is fit to a large database of responses produced by a mechanistic crystal plasticity based polycrystal model. Parameters for the LAROMance surrogate material model reflect the pedigree of the Grade 91 microstructure. Both material models have been integrated into the Grizzly code, based on the open-source MOOSE multiphysics simulation framework, to simulate both the progression of aging mechanisms and the effects of that aging on nuclear power plant structures. Grizzly is used analyze a three-dimensional Grade 91 piping system to compare the long-term inelastic response predicted by these two fundamentally different models and assess the sensitivity of the material model input parameters on this quantity of interest.

42 ENGINEERING↗
DOE OSTI2023

Electronic structure of Co 3 d states in the Kitaev material candidate honeycomb cobaltate Na 3 Co 2 SbO 6 probed with x-ray dichroism

The recent prediction that honeycomb lattices of Co 2+ (3d 7 ) ions could host dominant Kitaev interactions provides an exciting direction for exploration of new routes to stabilizing Kitaev’s quantum spin liquid in real materials. Na 3 Co 2 SbO 6 has been singled out as a potential material candidate provided that spin and orbital moments couple into a J eff = $\frac{1}{2}$ ground state, and that the relative strength of trigonal crystal field and spin-orbit coupling acting on Co ions can be tailored. Using x-ray linear dichroism (XLD) and x-ray magnetic circular dichroism (XMCD) experiments, alongside configuration interaction calculations, we confirm the counterintuitive positive sign of the trigonal crystal field acting on Co 2+ ions and test the validity of the J eff = $\frac{1}{2}$ description of the electronic ground state. Furthermore, the results lend experimental support to recent theoretical predictions that a compression (elongation) of CoO 6 octahedra along (perpendicular to) the trigonal axis would drive this cobaltate toward the Kitaev limit, assuming the J eff = $\frac{1}{2}$ character of the electronic ground state is preserved.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Prediction and Control of Atomic Ordering in Electrodeposited Binary Alloy Films: Direct Synthesis of L10 Magnetic Phases

Permanent magnet materials are essential components in renewable energy technology, underlying, electric vehicles, hybrids, wind turbines and more. Today commercial permanent magnets based on Nd-Fe-B exhibit an energy product of ~ 400 kJ/m 3 , capable to run wind turbines efficiently. Unfortunately, the supplies of rare-earth elements are centralized, and their availability may see volatility over time. This state of affairs stimulates the search for the search for rare-earth-free permanent magnetic materials. The L1 0 crystal structure underlines an important class of chemical ordered alloys that exhibit uniaxial magnetocrystalline anisotropy. This material has been considered an excellent candidate for rare-earth-free permanent magnets. In this program, a series of systematic experiments were performed and analysed in order to understand the effect of each. In order to synthesize L 10-Fe-Ni, one has to circumvent the sluggish kinetic and the limited driving force resulted from the low order-disorder temperature (~320°C) of the system. A strategy was found to trigger a martensitic transformation from the FCC phase to one of the non-cubic phases by high-strain methods. The body-centered tetragonal Fe-Ni obtained by this strategy was considered as a precursor of L10 Fe-Ni. The formation of BCT Fe-Ni via a displacive process was also implemented at the nanoscale by using tailored AuNi@FeNi core@shell.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Microstructure-Based Estimation of Strength and Ductility Distributions for \(\alpha +\beta \) Titanium Alloys

Titanium alloys are processed to develop a wide range of microstructure configurations and therefore material properties. While these properties are typically measured experimentally, a framework for property prediction could greatly enhance alloy design and manufacturing. In this work, a microstructure-sensitive framework is presented for the prediction of strength and ductility as well as estimates of the bounds in variability for these properties. The framework explicitly considers distributions of microstructure via new approaches for instantiation of structure in synthetic samples. The parametric evaluation strategy, including the finite element simulation package FEpX, is used to create and test virtual polycrystalline samples to evaluate the variability bounds of mechanical properties in Ti-6Al-4V. Critical parameters for the property evaluation framework are provided by measurements of single crystal properties and advanced characterization of microstructure and slip system strengths in 2D and 3D. Property distributions for yield strength and ductility are presented, along with the validation and verification steps undertaken. Comparisons between strain localization and slip activity in virtual samples and in experimental grain-scale strain measurements are also discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Comparison of Machine Learning Approaches for Prediction of the Equivalent Alkane Carbon Number for Microemulsions Based on Molecular Properties

The chemical properties of oils are vital in the design of microemulsion systems. The hydrophilic–lipophilic difference equation used to predict microemulsions’ phase behavior expresses the oils’ physiochemical properties as the equivalent alkane carbon number (EACN). The experimental determination of EACN requires knowledge of the temperature dependence of the microemulsion system and the effects of different surfactant concentrations. Thus, the experimental determination is time-intensive and tedious, requiring days to months for proper separations. Furthermore, the experiments require high purity of chemicals because microemulsions are sensitive to impurities. Our work focuses on the quick and reliable predictions of the EACN with machine learning (ML) models. Due to the immaturity of ML chemical predictions, we compare three graph neural networks (GNNs) and a gradient-boosted tree algorithm, known as XGBoost. The GNNs use the molecular structures represented as simplified molecular-input line-entry system (SMILES) codes for the initial input, which allows us to assess whether geometry optimization is necessary for reliable results. The XGBoost model also begins with the SMILES representations of the molecules but uses molecular descriptors instead of geometry optimizations. As a result, the best model tested (crystal graph convolutional neural network with Merck molecular force field-94) has an error of 1.15 EACN units of the true EACN for unknown data with the errors skewed toward zero and an R² score of 0.9

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Accelerating defect predictions in semiconductors using graph neural networks

First-principles computations reliably predict the energetics of point defects in semiconductors but are constrained by the expense of using large supercells and advanced levels of theory. Machine learning models trained on computational data, especially ones that sufficiently encode defect coordination environments, can be used to accelerate defect predictions. Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of group IV, III–V, and II–VI zinc blende semiconductors, powered by crystal Graph-based Neural Networks (GNNs) trained on high-throughput density functional theory (DFT) data. Using an innovative approach of sampling partially optimized defect configurations from DFT calculations, we generate one of the largest computational defect datasets to date, containing many types of vacancies, self-interstitials, anti-site substitutions, impurity interstitials and substitutions, as well as some defect complexes. We applied three types of established GNN techniques, namely crystal graph convolutional neural network, materials graph network, and Atomistic Line Graph Neural Network (ALIGNN), to rigorously train models for predicting defect formation energy (DFE) in multiple charge states and chemical potential conditions. We find that ALIGNN yields the best DFE predictions with root mean square errors around 0.3 eV, which represents a prediction accuracy of 98% given the range of values within the dataset, improving significantly on the state-of-the-art. We further show that GNN-based defective structure optimization can take us close to DFT-optimized geometries at a fraction of the cost of full DFT. The current models are based on the semi-local generalized gradient approximation-Perdew–Burke–Ernzerhof (PBE) functional but are highly promising because of the correlation of computed energetics and defect levels with higher levels of theory and experimental data, the accuracy and necessity of discovering novel metastable and low energy defect structures at the PBE level of theory before advanced methods could be applied, and the ability to train multi-fidelity models in the future with new data from non-local functionals. The DFT-GNN models enable prediction and screening across thousands of hypothetical defects based on both unoptimized and partially optimized defective structures, helping identify electronically active defects in technologically important semiconductors.

Rahman, Md Habibur (ORCID:000000027705984X)↗
DOE OSTI2025

Competing magnetic phases in Li-Fe-Ge kagome systems

Competing interlayer magnetic interactions in kagome magnets can lead to diverse magnetic phases, which enable promising topological or quantum material properties. In this work, the electronic structure and magnetic properties are studied using first-principles calculations for the Li⁢Fe6⁢G⁡e6, Li⁢Fe6⁢G⁡e4, and Li⁢Fe6⁢G⁡e5 compounds sharing the kagome F⁡e3⁢Ge layer motif but with different interlayer arrangements. For Li⁢Fe6⁢G⁡e4 and Li⁢Fe6⁢G⁡e5, the predicted magnetic ground states are collinear antiferromagnetic (AFM) states involving a mix of ferromagnetic (FM) and AFM interlayer orientations. In contrast, for Li⁢Fe6⁢G⁡e6, an incommensurate cycloidal spin spiral ground state is stabilized, although its energy is close to a collinear A-type AFM state. The analysis of magnetic RKKY exchange coupling confirms the results of calculations. The values of atomic magnetic moments are in good agreement with existing experimental estimations. Our experiments on Li⁢Fe6⁢G⁡e6 single crystals reveals AFM ordering at ∼540 K and transition to another magnetic phase with a small FM component, possibly with spin canting, below ∼270 K.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Structural Diversity and Magnetic Properties of Hybrid Ruthenium Halide Perovskites and Related Compounds

There has been a great deal of recent interest in extended compounds containing Ru 3+ and Ru 4+ in light of their range of unusual physical properties. Many of these properties are displayed in compounds with the perovskite and related structures. Here we report an array of structurally diverse hybrid ruthenium halide perovskites and related compounds: MA 2 RuX 6 (X=Cl or Br), MA 2 MRuX 6 (M=Na, K or Ag; X=Cl or Br) and MA 3 Ru 2 X 9 (X=Br) based upon the use of methylammonium (MA=CH 3 NH 3 + ) on the perovskite A site. The compounds MA 2 RuX 6 with Ru 4+ crystallize in the trigonal space group R3¯m and can be described as vacancy-ordered double-perovskites. The ordered compounds MA 2 MRuX 6 with M + and Ru 3+ crystallize in a structure related to BaNiO 3 with alternating MX 6 and RuX 6 face-shared octahedra forming linear chains in the trigonal P3¯m space group. The compound MA 3 Ru 2 Br 9 crystallizes in the orthorhombic Cmcm space group and displays pairs of face-sharing octahedra forming isolated Ru 2 Br 9 moieties with very short Ru–Ru contacts of 2.789 Å. The structural details, including the role of hydrogen bonding and dimensionality, as well as the optical and magnetic properties of these compounds are described. The magnetic behavior of all three classes of compounds is influenced by spin–orbit coupling and their temperature-dependent behavior has been compared with the predictions of the appropriate Kotani models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Heteroepitaxial Integration of ZnGeN 2 on GaN Buffers Using Molecular Beam Epitaxy

Recently theorized hybrid II-IV-N 2 /III-N heterostructures, based on current commercialized (In,Ga)N devices, are predicted to significantly advance the design space of highly efficient optoelectronics in the visible spectrum, yet there are few epitaxial studies of II-IV-N 2 materials. In this work, we present heteroepitaxial ZnGeN 2 grown on GaN buffers and AlN templates. We demonstrate that a GaN nucleating surface is crucial for increasing the ZnGeN 2 crystallization rate to combat Zn desorption, extending the stoichiometric growth window from 215 degrees C on AlN to 500 degrees C on GaN buffers. Structural characterization reveals well-crystallized films with threading dislocations extending from the GaN buffer. These films have a critical thickness for relaxation of 20-25 nm as determined by reflection high energy electron diffraction (RHEED) and cross-sectional scanning electron microscopy (SEM). The films exhibit a cation-disordered wurtzite structure, with lattice constants a = 3.216 +/- 0.004 A and c = 5.215 +/- 0.005 A determined by RHEED and X-ray diffraction (XRD). This work demonstrates a significant step toward the development of hybrid ZnGeN 2 -GaN integrated devices.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Predicting Quantum Criticality in Single-Crystalline Ce 2 Ru 3 Ge 5

Strongly correlated f-electron systems are known to host exotic quantum states, such as quantum criticality, complex order parameters, and unconventional superconductivity. However, the appearance of these exotic states is difficult to predict, making the study of quantum critical behavior challenging, especially in ferromagnetic materials. Herein, we report a structure–property map for Ce 2 M 3 X 5 (M = transition metal; X = main group element) that aids in the targeted design of materials likely to exhibit quantum criticality. Guided by this map, we report on the synthesis of single-crystalline Ce 2 Ru 3 Ge 5 and provide, for the first time, magnetic susceptibility, heat capacity, resistivity, and magnetoresistance measurements on single crystals. Here, we observe a weak ferromagnetic-like response at 7.5 K, which is contrasted with the bulk ferromagnetic ordering that appears in polycrystalline samples. Non-Fermi liquid behavior is seen in the temperature dependent electrical resistivity and heat capacity of the single crystals, suggesting proximity to a ferromagnetic quantum critical point without chemical or physical pressure. Given the contrast with previous reports of polycrystals, these results lead us to propose that single crystalline Ce 2 Ru 3 Ge 5 is intrinsically tuned into the vicinity of a ferromagnetic quantum critical point.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Database and deep-learning scalability of anharmonic phonon properties by automated brute-force first-principles calculations

Understanding the anharmonic phonon properties of crystal compounds—such as phonon lifetimes and thermal conductivities—is essential for investigating and optimizing their thermal transport behaviors. These properties also impact optical, electronic, and magnetic characteristics through interactions between phonons and other quasiparticles and fields. In this study, we develop an automated first-principles workflow to calculate anharmonic phonon properties and build a comprehensive database encompassing more than 6500 inorganic compounds. Utilizing this dataset, we train a graph neural network model to predict thermal conductivity values and spectra from structural parameters, demonstrating a scaling law in which prediction accuracy improves with increasing training data size. High-throughput screening with the model enables the identification of materials exhibiting extreme thermal conductivities—both high and low. The resulting database offers valuable insights into the anharmonic behavior of phonons, thereby accelerating the design and development of advanced functional materials.

Ohnishi, Masato [University of Tokyo (Japan); Inst↗
DOE OSTI2023

Charge density wave order, local lattice distortions, and topological electronic states in Nb Te 4

Assessing the atomic and electronic structure of strongly correlated systems in which the crystal symmetry changes due to emergent lattice distortions, such as charge density waves (CDWs), is nontrivial because the distortions are not necessarily amenable to a traditional crystallographic description. Using advance scattering and modeling techniques, we reveal the evolution of the atomic displacement modes behind the CDW phases of quasi-one-dimensional NbTe 4 and also derive the so far unknown atomic structure of its low-temperature commensurate (C) CDW phase. Electronic structure calculations based on the experimental C-CDW structure data predict the existence of Dirac fermions whose multiplicity turns out to depend on the degree of lattice distortions accounted for in the experimental structure derivation. Finally, we argue that the electronic structure of CDW systems in general, and in particular in transition metal tetra-tellurides, can be strongly impacted by local lattice distortions and, therefore, they should be fully accounted for when their rich physics is considered.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Geometric Requirements for Modeling Polycrystalline Representative Volume Elements Using the Generalized Method of Cells

To accurately predict the mechanical response of advanced metallic parts, the grain structure of the metal must be considered and coupled to the macroscale in a multiscale model. The size of the microscale representative volume element must be sufficiently large, and the grid used to discretize the microscale must be adequately refined, in order to ensure an accurate and converged solution. The generalized method of cells is used to predict the mechanical properties of polycrystalline representative volume elements of copper containing randomly oriented grains with the properties of a single face-centered-cubic crystal. Moreover, the results of a parametric study are used to conclude the geometric requirements for an objective microscale model, with respect to the elastic stiffness.

Multiscale Modeling↗