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Results for “Materials Modeling, Density Functional Theory, Computational Materials Science”

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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Magnetic structure, excitations and short-range order in honeycomb Na 2 Ni 2 TeO 6

Na 2 Ni 2 TeO 6 has a layered hexagonal structure with a honeycomb lattice constituted by Ni 2+ and a chiral charge distribution of Na + that resides between the Ni layers. In the present work, the antiferromagnetic transition temperature of Na 2 Ni 2 TeO 6 is confirmed at TN˜ 27 K, and further, it is found to be robust up to 8 T magnetic field and 1.2 GPa external pressure; and, without any frequency-dependence. Slight deviations from nominal Na-content (up to 5%) does not seem to influence the magnetic transition temperature, TN. Isothermal magnetization curves remain almost linear up to 13 T. Our analysis of neutron diffraction data shows that the magnetic structure of Na 2 Ni 2 TeO 6 is faithfully described by a model consisting of two phases described by the commensurate wave vectors vec kc, (0.5 0 0) and (0.5 0 0.5), with an additional short-range order component incorporated in to the latter phase. Consequently, a zig-zag long-range ordered magnetic phase of Ni 2+ results in the compound, mixed with a short-range ordered phase, which is supported by our specific heat data. Theoretical computations based on density functional theory (DFT) predict predominantly in-plane magnetic exchange interactions that conform to a J1-J2-J3 model with a strong J3 term. The computationally predicted parameters lead to a reliable estimate for TN and the experimentally observed zig-zag magnetic structure. A spin wave excitation in Na 2 Ni 2 TeO 6 at E ˜ 5 meV at T = 5 K is mapped out through inelastic neutron scattering experiments, which is reproduced by linear spin wave theory calculations using the J values from our computations. Our specific heat data and inelastic neutron scattering data strongly indicate the presence of short-range spin correlations, at T > TN, stemming from incipient antiferromagnetic clusters.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Machine learning guided prediction of solute segregation at coherent and semi-coherent metal/oxide interfaces

Investigation of semi-coherent metal/oxide interfaces with misfit dislocations using density functional theory (DFT) is computationally intensive to the point of being prohibitive, as it involves several hundreds to many thousands of atoms. In this study, we examined the solute segregation behavior at the Fe/Y 2 O 3 interface—a model interface for cladding applications in nuclear fission reactors—using a combination of DFT calculations and machine learning (ML) approaches. Both coherent and semi-coherent interfaces were considered. ML models were trained on DFT-calculated segregation energies to identify the key chemical, geometric and strain energy related features that govern solute segregation behavior at coherent Fe/Y 2 O 3 interfaces. Furthermore, it was found that ML models when trained on DFT calculated segregation energy of elements at a coherent interface, comprising of about a hundred-atom supercell, can predict the segregation energy of elements at a semi-coherent Fe/Y 2 O 3 interface (with multiple hundreds of atoms) at a fraction of computational cost (1/35th), with an accuracy comparable to DFT calculations.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Ultra-fast interpretable machine-learning potentials

Abstract All-atom dynamics simulations are an indispensable quantitative tool in physics, chemistry, and materials science, but large systems and long simulation times remain challenging due to the trade-off between computational efficiency and predictive accuracy. To address this challenge, we combine effective two- and three-body potentials in a cubic B-spline basis with regularized linear regression to obtain machine-learning potentials that are physically interpretable, sufficiently accurate for applications, as fast as the fastest traditional empirical potentials, and two to four orders of magnitude faster than state-of-the-art machine-learning potentials. For data from empirical potentials, we demonstrate the exact retrieval of the potential. For data from density functional theory, the predicted energies, forces, and derived properties, including phonon spectra, elastic constants, and melting points, closely match those of the reference method. The introduced potentials might contribute towards accurate all-atom dynamics simulations of large atomistic systems over long-time scales.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Sensing at the Nanoscale Using Nitrogen-Vacancy Centers in Diamond: A Model for a Quantum Pressure Sensor

The sensing of stress under harsh environmental conditions with high resolution has critical importance for a range of applications including earth’s subsurface scanning, geological CO2 storage monitoring, and mineral and resource recovery. Using a first-principles density functional theory (DFT) approach combined with the theoretical modelling of the low-energy Hamiltonian, here, we investigate a novel approach to detect unprecedented levels of pressure by taking advantage of the solid-state electronic spin of nitrogen-vacancy (NV) centers in diamond. We computationally explore the effect of strain on the defect band edges and band gaps by varying the lattice parameters of a diamond supercell hosting a single NV center. A low-energy Hamiltonian is developed that includes the effect of stress on the energy level of a ±1 spin manifold at the ground state. By quantifying the energy level shift and split, we predict pressure sensing of up to 0.3 MPa/Hz using the experimentally measured spin dephasing time. We show the superiority of the quantum sensing approach over traditional optical sensing techniques by discussing our results from DFT and theoretical modelling for the frequency shift per unit pressure. Importantly, we propose a quantum manometer that could be useful to measure earth’s subsurface vibrations as well as for pressure detection and monitoring in high-temperature superconductivity studies and in material sciences. Our results open avenues for the development of a sensing technology with high sensitivity and resolution under extreme pressure limits that potentially has a wider applicability than the existing pressure sensing technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2022

High-Accuracy Semiempirical Quantum Models Based on a Minimal Training Set

A great need exists for computationally efficient quantum simulation approaches that can achieve an accuracy similar to high-level theories at a fraction of the computational cost. In this regard, we have leveraged a machine-learned interaction potential based on Chebyshev polynomials to improve density functional tight binding (DFTB) models for organic materials. The benefit of our approach is two-fold: (1) many-body interactions can be corrected for in a systematic and rapidly tunable process, and (2) high-level quantum accuracy for a broad range of compounds can be achieved with ~0.3% of data required for one advanced deep learning potential. Our model exhibits both transferability and extensibility through comparison to quantum chemical results for organic clusters, solid carbon phases, and molecular crystal phase stability rankings. Overall, our efforts thus allow for high-throughput physical and chemical predictions with up to coupled-cluster accuracy for systems that are computationally intractable with standard approaches.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Cu–Ni Oxidation Mechanism Unveiled: A Machine Learning-Accelerated First-Principles and in Situ TEM Study

Here, the development of accurate methods for determining how alloy surfaces spontaneously restructure under reactive and corrosive environments is a key, long-standing, grand challenge in materials science. Using machine learning-accelerated density functional theory and rare-event methods, in conjunction with in situ environmental transmission electron microscopy (ETEM), we examine the interplay between surface reconstructions and preferential segregation tendencies of CuNi(100) surfaces under oxidation conditions. Our modeling approach predicts that oxygen-induced Ni segregation in CuNi alloys favors Cu(100)-O c(2 × 2) reconstruction and destabilizes the Cu(100)-O (2√2 × √2)R45° missing row reconstruction (MRR). In situ ETEM experiments validate these predictions and show Ni segregation followed by NiO nucleation and growth in regions without MRR, with secondary nucleation and growth of Cu 2 O in MRR regions. Our approach based on combining disparate computational components and in situ ETEM provides a holistic description of the oxidation mechanism in CuNi, which applies to other alloy systems.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Li1−xNiO2 Many-body DMC Benchmark Dataset

The dataset contains all numerical data generated in support of the manuscript “Many‑body Benchmark of Electronic Charge and Spin Densities for Li1–xNiO2​” (Journal of Chemical Theory and Computation, DOI: 10.1021/acs.jctc.5c02097, URL: https://pubs.acs.org/doi/10.1021/acs.jctc.5c02097). The materials included in this repository are: 1. Data files used to produce all figures and tables in the main manuscript and supporting information. 2. Benchmark density‑functional theory (DFT) datasets used for the charge‑ and spin‑density analyses. 3. Reference many‑body diffusion Monte Carlo (DMC) calculations and associated input/output files.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Hybrid programming-model strategies for GPU offloading of electronic structure calculation kernels

To address the challenge of performance portability and facilitate the implementation of electronic structure solvers, we developed the basic matrix library (BML) and Parallel, Rapid O(N), and Graph-based Recursive Electronic Structure Solver (PROGRESS) library. The BML implements linear algebra operations necessary for electronic structure kernels using a unified user interface for various matrix formats (dense and sparse) and architectures (CPUs and GPUs). Focusing on density functional theory and tight-binding models, PROGRESS implements several solvers for computing the single-particle density matrix and relies on BML. In this paper, we describe the general strategies used for these implementations on various computer architectures, using OpenMP target functionalities on GPUs, in conjunction with third-party libraries to handle performance critical numerical kernels. In this study, we demonstrate the portability of this approach and its performance in benchmark problems.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Revealing the Origin and Nature of the Buried Metal‐Substrate Interface Layer in Ta/Sapphire Superconducting Films

Abstract Despite constituting a smaller fraction of the qubit's electromagnetic mode, surfaces and interfaces can exert significant influence as sources of high‐loss tangents, which brings forward the need to reveal properties of these extended defects and identify routes to their control. Here, we examine the structure and composition of the metal‐substrate interfacial layer that exists in Ta/sapphire‐based superconducting films. Synchrotron‐based X‐ray reflectivity measurements of Ta films, commonly used in these qubits, reveal an unexplored interface layer at the metal‐substrate interface. Scanning transmission electron microscopy and core‐level electron energy loss spectroscopy identified an intermixing layer (≈0.65 ± 0.05 nm) at the metal‐substrate interface containing Al, O, and Ta atoms. Density functional theory modeling reveals that the structure and properties of the Ta/sapphire heterojunctions are determined by the oxygen content on the sapphire surface prior to Ta deposition for two atomic terminations of sapphire. Using a multimodal approach, we gained deeper insights into the interface layer between the metal and substrate, which suggests that the orientation of deposited Ta films depend on the surface termination of sapphire. The observed elemental intermixing at the metal‐substrate interface influences the thermodynamic stability and electronic behavior of the film, which may also affect qubit performance.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Theoretical Prediction of Thermal Expansion Anisotropy for Y 2 Si 2 O 7 Environmental Barrier Coatings Using a Deep Neural Network Potential and Comparison to Experiment

Environmental barrier coatings (EBCs) are an enabling technology for silicon carbide (SiC)-based ceramic matrix composites (CMCs) in extreme environments such as gas turbine engines. However, the development of new coating systems is hindered by the large design space and difficulty in predicting the properties for these materials. Density Functional Theory (DFT) has successfully been used to model and predict some thermodynamic and thermo-mechanical properties of high-temperature ceramics for EBCs, although these calculations are challenging due to their high computational costs. In this work, we use machine learning to train a deep neural network potential (DNP) for Y 2 Si 2 O 7 , which is then applied to calculate the thermodynamic and thermo-mechanical properties at near-DFT accuracy much faster and using less computational resources than DFT. We use this DNP to predict the phonon-based thermodynamic properties of Y 2 Si 2 O 7 with good agreement to DFT and experiments. We also utilize the DNP to calculate the anisotropic, lattice direction-dependent coefficients of thermal expansion (CTEs) for Y 2 Si 2 O 7 . Molecular dynamics trajectories using the DNP correctly demonstrate the accurate prediction of the anisotropy of the CTE in good agreement with the diffraction experiments. In the future, this DNP could be applied to accelerate additional property calculations for Y 2 Si 2 O 7 compared to DFT or experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Accurate tight-binding model for twisted bilayer graphene describes topological flat bands without geometric relaxation

A major hurdle in understanding the phase diagram of twisted bilayer graphene is the roles of lattice relaxation and electronic structure on isolated band flattening near magic twist angles. Here in this work, the authors develop an accurate local environment tight-binding model fit to tight-binding parameters computed from ab initio density-functional theory calculations across many atomic configurations. With the accurate parametrization, it is found that the magic angle shifts to slightly lower angles than often quoted, from around 1.05° to around 0.99°, and that isolated flat bands appear for rigidly rotated graphene layers, with enhancement of the flat bands when the layers are allowed to distort. Study of the orbital localization supports the emergence of fragile topology in the isolated flat bands without the need for lattice relaxation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The ab initio non-crystalline structure database: empowering machine learning to decode diffusivity

Non-crystalline materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-driven exploration and design of non-crystalline materials is hampered by the absence of a comprehensive database covering a broad chemical space. In this work, we present the largest computed non-crystalline structure database to date, generated from systematic and accurate ab initio molecular dynamics (AIMD) calculations. We also show how the database can be used in simple machine-learning models to connect properties to composition and structure, here specifically targeting ionic conductivity. These models predict the Li-ion diffusivity with speed and accuracy, offering a cost-effective alternative to expensive density functional theory (DFT) calculations. Furthermore, the process of computational quenching non-crystalline structures provides a unique sampling of out-of-equilibrium structures, energies, and force landscape, and we anticipate that the corresponding trajectories will inform future work in universal machine learning potentials, impacting design beyond that of non-crystalline materials. In addition, combining diffusion trajectories from our dataset with models that predict liquidus viscosity and melting temperature could be utilized to develop models for predicting glass-forming ability.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Dependence of the Elastic Stiffness Tensors of PETN, α‐RDX, γ‐RDX, ϵ‐RDX, ϵ‐CL‐20, DAAF, FOX‐7, and β‐HMX on Hydrostatic Compression

Abstract The dependence of the components of the elastic stiffness tensors (or elastic constants) of the organic explosives PETN, RDX, CL‐20, DAAF, FOX‐7, and HMX on hydrostatic pressure up to 10 GPa have been computed using dispersion‐corrected density functional theory. We report the evolution of lattice parameters and the non‐zero stiffnesses for the tetragonal, orthorhombic, and monoclinic crystal symmetries. Linear and quadratic dependencies of the components of the elastic stiffness tensors on volumetric compression and hydrostatic pressure are tabulated for use in single crystal plasticity models.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A prediction of the thermodynamic, thermophysical, and mechanical properties of CrTaO 4 from first principles

It is reported that the self-forming CrTaO 4 oxide scale can protect refractory high-entropy alloys from oxidation, superior to Cr 2 O 3 . In this paper, the phase stability, mechanical, and thermal properties of three polymorphous phases of CrTaO 4 are systematically investigated from first-principles density functional theory calculations. The mechanical properties predicted using the strain–energy methods indicated that all three phases are mechanically stable. The temperature dependence of elastic constants and polycrystalline moduli of three phases demonstrated the thermal softening as temperature increase. The Helmholtz-free energies as a function of volume and temperature are derived from phonon dispersions within the quasi-harmonic approximation at six strained volumes. The calculated apparent bulk coefficients of thermal expansion of these three phases are evaluated, the highest value approximately 13.4× 10 –6 K –1 within a temperature range of 500–2000 K for the rutile I4 1 md phase. The lattice thermal conductivity calculated by the Debye–Callaway model suggested that the rutile type I4 1 md phase has the lowest value of approximately 2.1 W/m/K at 1800 K. The other two phases, C2/m and P2/c, exhibit higher values due to relatively lower Grüneisen parameters and larger phonon velocities. The melting point of CrTaO 4 is predicted to be between 1975 and 2449 K using ab initio molecular dynamics simulations. Finally, this work provides a comprehensive theoretical understanding of the thermodynamic, mechanical, and thermal properties for the new material CrTaO 4 and serves as an example of a viable computational design strategy for improved oxidation resistance of refractory alloys at high temperatures.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Effect of applied potential on metal surfaces: Surface energy, Wulff shape and charge distribution

Here we use grand canonical density functional theory to predict the surface energies, Wulff shapes, charge distributions and catalytically active sites of different metal surfaces under electrochemical conditions. We propose a method for computing surface energies from grand canonical density functional theory (GC-DFT) calculations of periodic slab models and use it to compute the surface energies of the facets of Pt, Cu, and Ag crystals to predict their Wulff shapes under electrochemical conditions. GC-DFT predicts that, for the pure metals studied, solvation only slightly affects the Wulff shape while applied potentials considerably affect the surface energies and corresponding Wulff shapes. We used Bader charge analysis of GC-DFT computed electron densities to investigate the effect of applied potential on the distribution of electron density over the atoms of the surfaces of Pt, Cu, Ag, and the 75–25 Ag-Pt and Au-Ni alloys. This analysis shows that, under an applied potential, the electron density is unevenly distributed over the surface atoms and that the charges of atoms more exposed to solvent are more sensitive to bias. Our results show that the most sensitive atom to bias can be used to identify the most favorable adsorption site and thus, the active sites of electrochemical reactions, which is computationally less demanding than calculating the adsorption energies on all possible adsorption sites.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Orthorhombic Structures as Inorganic Halide Perovskite Models for High-Throughput DFT Investigations

Although high-symmetry Pm-3m space group cubic models are computationally efficient for high-throughput density functional theory (DFT) calculations of inorganic ternary (ABX 3 ) halide perovskites (HPs), they frequently predict band gaps (E g ) that disagree with experiment. Conversely, while low-symmetry cubic polymorphous networks (PN) comprised of 160 to 320 atoms incur significantly greater computational cost, they predict E g ’s that are more closely aligned with experiment. Here, in this study, we compare the DFT total energies and E g ’s predicted by four high-symmetry structure models (Pnma orthorhombic, R3m trigonal, P4/mbm tetragonal, and Pm-3m cubic) to cubic PNs for 5 experimentally characterized ternary HPs and find that the orthorhombic model computes Eg’s with the smallest MAD of 0.23 eV relative to the PNs. Pair distribution functions and DFT-computed total energies show that octahedral tilting, which is present in the 20-atom orthorhombic and 160-atom cubic PN models but not in the 5-atom cubic models, stabilizes all 5 compositions in our benchmarking set. We also find that imposing PN constraints when generating and optimizing these orthorhombic structures by fixing the unit cell lattice vectors and displacing the atoms prior to ionic relaxation with DFT, which we call the orthorhombic surrogate model (OSM), lowers the MAD of E g predictions to 0.09 eV. Our OSM predicts the PN band gaps of an additional 95 theoretical inorganic ternary HPs with MAD of 0.08 eV, supporting its usage in high-throughput DFT investigations to closely estimate PN band gaps with much less computational expense.

14 SOLAR ENERGY↗
DOE OSTI2021

Tuning magnetic antiskyrmion stability in tetragonal inverse Heusler alloys

The identification of materials supporting complex, tunable magnetic order at ambient temperatures is foundational to the development of new magnetic device architectures. In this work, we report the design of Mn 2 XY tetragonal inverse Heusler alloys that are capable of hosting magnetic antiskyrmions whose stability is sensitive to elastic strain. Here We first construct a universal magnetic Hamiltonian capturing the short- and long-range magnetic order which can be expected in these materials. This model reveals critical combinations of magnetic interactions that are necessary to approach a magnetic phase boundary, where the magnetic structure is highly susceptible to small perturbations such as elastic strain. We then computationally search for quaternary Mn 2 (X 1 , X 2 ) Y alloys where these critical interactions may be realized and which are likely to be synthesizable in the inverse Heusler structure. We identify the Mn 2 Pt 1–z X z Ga family of materials with X = Au, Ir, Ni as an ideal system for accessing all possible magnetic phases, with several critical compositions where magnetic phase transitions may be actuated mechanically.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Computational modeling of grain boundary segregation: A review

Nearly all metals, alloys, ceramics, and their associated composites are polycrystalline in nature, with grain boundaries that separate well-defined crystalline regions that influence materials properties. In all but the most pure elemental systems, intentional solutes or impurities are present and can segregate to, or less commonly away from, the grain boundaries, in turn influencing boundary behavior, their stability, and associated materials properties. In some cases, grain-boundary segregation can also trigger “phase-like” structural transitions that dramatically alter the essential nature of the boundary. With the development of advanced electron microscopy techniques, researchers can directly observe grain-boundary structures and segregation with atomic precision. Despite such spatial resolution, the underlying mechanisms governing grain-boundary segregation remain difficult to characterize. As a result, computational modeling techniques such as density functional theory, molecular dynamics, mesoscale phase-field, continuum defect theory, and others are important complementary tools to experimental observations for studying grain-boundary segregation behavior. In conclusion, these computational methods offer the ability to explore the underlying formation mechanisms of grain-boundary segregation, elucidate complex segregation behavior, and provide insights into solutions to effectively controlling microstructure.

36 MATERIALS SCIENCE↗