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At least 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

High-Temperature Thermodynamics of Uranium from Ab Initio Modeling

We present high-temperature thermodynamic properties for uranium in its γ phase (γ-U) from first-principles, relativistic, and anharmonic theory. The results are compared to CALPHAD modeling. The ab initio electronic structure is obtained from density-functional theory (DFT) that includes spin–orbit coupling and an added self-consistent orbital-polarization (OP) mechanism for more accurate treatment of magnetism. The first-principles method is coupled to a lattice dynamics scheme that is used to model anharmonic lattice vibrations, namely, Self-Consistent Ab Initio Lattice Dynamics (SCAILD). The methodology can be summarized in the acronym DFT + OP + SCAILD. Upon thermal expansion, γ-U develops non-negligible magnetic moments that are included for the first time in thermodynamic theory. The all-electron DFT approach is shown to model γ-U better than the commonly used pseudopotential method. In addition to CALPHAD, DFT + OP + SCAILD thermodynamic properties are compared with other ab initio and semiempirical modeling and experiments. Our first-principles approach produces Gibbs free energy that is essentially identical to CALPHAD. The DFT + OP + SCAILD heat capacity is close to CALPHAD and most experimental data and is predicted to have a significant thermal dependence due to the electronic contribution.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Many-body theory of phonon-induced spin relaxation and decoherence

First-principles calculations enable accurate predictions of electronic interactions and dynamics. However, computing the electron spin dynamics remains challenging. The spin-orbit interaction causes various dynamical phenomena that couple with phonons, such as spin precession and spin-flip e-ph scattering, which are difficult to describe with current first-principles calculations. Here, in this work, we show a rigorous framework to study phonon-induced spin relaxation and decoherence, by computing the spin-spin correlation function and its vertex corrections due to e-ph interactions. We apply this approach to a model system and develop corresponding first-principles calculations of spin relaxation in GaAs. Our vertex-correction formalism is shown to capture the Elliott-Yafet, Dyakonov-Perel, and strong-precession mechanisms—three independent spin decoherence regimes with distinct physical origins—thereby unifying their theoretical treatment and calculation. Our method is general and enables quantitative studies of spin relaxation, decoherence, and transport in a wide range of materials and devices.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Electronic structure of Humble defects in Ge and Ge 0.8⁢ Si 0.2

The group-IV diamond-structure elements are known to host a variety of planar defects, including {001} planar defects in C and {001}, {111}, and {113} planar defects in Si and Ge. Among the {001} planar defects, the Humble defect, known for some time to occur in Ge, has recently also been observed in Si-Ge alloys, but the details of its electronic structure remain poorly understood. Here, in this work, we perform first-principles density-functional calculations to study Humble defects in both Ge and Ge 0.8⁢ Si 0.2 . We also measure the Si L 2,3 -edge electron energy-loss spectra both at the defect and in a bulk-like region far from the defect and compare with theoretical calculations on corresponding Si sites in our first-principles calculations. We find that inclusion of core-hole effects in the theory is essential for reproducing the observed L 2,3 edge spectra, and that once they are included, the results provide a set of fingerprints for different types of local atomic bonding environments in Ge 0.8⁢ Si 0.2 . Our first-principles calculations reveal that the Humble defects have a tendency to enlarge the electronic band gap, which may have potential uses in band engineering. The use of hybrid functionals for an improved description of the band gap in these systems is also discussed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Insulating antiferromagnetism in VTe

Here, we report a detailed theoretical and experimental study on the vanadium monotelluride VTe, which crystallizes in the NiAs hexagonal structure. First-principles calculations reveal a complex hierarchy of magnetic interactions and energy scales, with the ground state theoretically determined as an ($\frac{1}{2}$, 0, $\frac{1}{2}$) antiferromagnetic ordering with insulating character and a band gap of 0.5 eV. Experimental synthesis and characterization efforts find a substantially off-stoichiometric orthorhombic structure (a defect NiAs structure) with composition V 0.85 Te, and an apparent Néel point of some 45 K. First-principles calculations find good agreement with the observed Néel point. We also give an extended examination of the effects of off-stoichiometry on the calculated energetics, finding significant volume-related effects. Our first-principles calculations find the stoichiometric phase VTe to have a negative vanadium defect formation energy of over 1 eV, thus explaining the formation of the off-stoichiometric phase. Finally, we provide a structural explanation for the formation of defect structures in this and numerous other NiAs-structure materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Structural and vibrational properties of methane up to 71 GPa

Single-crystal synchrotron x-ray diffraction, Raman spectroscopy, and first principles calculations have been used to identify the structure of the high-pressure (HP) phase of molecular methane above 20 GPa up to 71 GPa at room temperature. The structure of the HP phase is trigonal R3, which can be represented as a distortion of the cubic phase B, previously documented at 7–15 GPa and confirmed here. The positions of hydrogen atoms in the HP phase have been obtained from first principles calculations, which also demonstrated the stability of this structure above 260 K at 25 GPa. The molecules occupy four different crystallographic sites in phase B and 11 sites in the HP phase, which result in splitting of molecular stretching modes detected in Raman spectroscopy and assigned here based on a good agreement with the Raman spectra calculated from the first principles. Furthermore, our study points out to a single HP phase unlike up to three HP phases proposed previously based on the Raman spectroscopy results only.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Mapping the three-dimensional fermiology of the triangular lattice magnet EuAg 4 Sb 2

In this paper, we report the temperature-field phase diagram as well as present a comprehensive study of the electronic structure and three-dimensional fermiology of the triangular-lattice magnet EuAg 4 ⁢Sb 2 , utilizing quantum oscillation measurements, angle-resolved photoemission spectroscopy, and first-principles calculations. The complex magnetic phase diagram of EuAg 4 ⁢Sb 2 highlights many transitions through nontrivial AFM states. Shubnikov-de Haas and de Haas-van Alphen oscillations were observed in the polarized ferromagnetic state of EuAg 4 ⁢Sb 2 , revealing three pairs of distinct spin-split frequency branches with small effective masses. A comparison of the angle-dependent oscillation data with first-principles calculations in the ferromagnetic state and angle-resolved photoemission spectra shows good agreement, identifying tubular hole pockets and hourglass-shaped hole pockets at the Brillouin zone center, as well as diamond-shaped electron pockets at the zone boundary. As the temperature increases, the frequency branches of the tiny hourglass pockets evolve into a more cylindrical shape, while the larger pockets remain unchanged. This highlights that variations in exchange splitting, driven by changes in the magnetic moment, primarily impact the small Fermi pockets without significantly altering the overall band structure. As a result, this is consistent with first-principles calculations, which show minimal changes near the Fermi level across ferromagnetic and simple antiferromagnetic states or under varying on-site Coulomb repulsion.

36 MATERIALS SCIENCE↗
DOE OSTI2022

High Throughput Computational Framework of Materials Properties for Extreme Environments

This project aims to establish a framework capable of efficiently predicting the properties of structural materials for service in harsh environments over a wide range of temperatures and over long periods of time. The approach is to develop and integrate high throughput first-principles calculations in combination with machine learning (ML) methods, perform high throughput CALPHAD (calculations of phase diagrams) modeling, and carry out finite element method (FEM) simulations. Relevant to high temperature service in fossil power system, nickel-based superalloys such as Inconel 740 and Haynes 282 as well as the associated (Ni-Cr-Co)-Al-C-Fe-Mn-Mo-Nb-Si-Ti system, were investigated. The present framework was built on the concept of phase-based property data, in which properties of individual phases are modeled as a function of internal and external independent variables. This project established an open-source infrastructure with the following capabilities: (1) High throughput implementation of first-principles calculations at finite temperatures and variable compositions using both accurate phonon calculations and the efficient Debye model for thermodynamic properties, elastic constants, diffusion coefficients, vacancy formation, stacking and twin faults, and dislocation mobility; i.e., using the developed code DFTTK; (2) Machine learning capabilities to predict the above properties so that the number of first-principles calculations can be significantly reduced; e.g., using the developed code SIPFENN; (3) High throughput CALPHAD modeling of the above properties as a function of temperature and composition using our unique capability based on ESPEI and PyCalphad; (4) New capabilities to predict the stress-strain behavior of individual phases; and (5) New models for tensile strength prediction in common FEM software with the crystal plasticity finite element simulations (CPFEM).

, Ni-based superalloys↗
DOE OSTI2021

Evaluating the Curtailment Risk of Non-Firm Utility-Scale Solar Photovoltaic Plants under a Novel Last-In First-Out Principle of Access Interconnection Agreement

With the increasing share of distributed energy resources on the electric grid, utility companies are facing significant decisions about infrastructure upgrades. An alternative to extensive and capital-intensive upgrades is to offer non-firm interconnection opportunities to distributed generators, via a coordinated operation of utility scale resources. This paper introduces a novel flexible interconnection option based on the last-in, first-out principles of access aimed at minimizing the unnecessary non-firm generation energy curtailment by balancing access rights and contribution to thermal overloads. Although we focus on solar photovoltaic (PV) plants in this work, the introduced flexible interconnection option applies to any distributed generation technology. The curtailment risk of individual non-firm PV units is evaluated across a range of PV penetration levels in a yearlong quasi-static time-series simulation on a real-world feeder. The results show the importance of the size of the curtailment zone in the curtailment risk distribution among flexible generation units as well as that of the “access right” defined by the order in which PV units connect to the grid. Case study results reveal that, with a proper selection of curtailment radius, utilities can reduce the total curtailment of flexible PV resources by up to more than 45%. Findings show that non-firm PV generators can effectively avoid all thermal limit-related upgrade costs.

14 SOLAR ENERGY↗
DOE OSTI2022

Machine learning molecular dynamics simulations toward exploration of high-temperature properties of nuclear fuel materials: case study of thorium dioxide

Predicting materials properties of nuclear fuel compounds is a challenging task in materials science. Their thermodynamical behaviors around and above the operational temperature are essential for the design of nuclear reactors. However, they are not easy to measure, because the target temperature range is too high to perform various standard experiments safely and accurately. Moreover, theoretical methods such as first-principles calculations also suffer from the computational limitations in calculating thermodynamical properties due to their high calculation-costs and complicated electronic structures stemming from f-orbital occupations of valence electrons in actinide elements. Here, we demonstrate, for the first time, machine-learning molecular-dynamics to theoretically explore high-temperature thermodynamical properties of a nuclear fuel material, thorium dioxide. The target compound satisfies first-principles calculation accuracy because f-electron occupation coincidentally diminishes and the scheme meets sampling sufficiency because it works at the computational cost of classical molecular-dynamics levels. We prepare a set of training data using first-principles molecular dynamics with small number of atoms, which cannot directly evaluate thermodynamical properties but captures essential atomistic dynamics at the high temperature range. Then, we construct a machine-learning molecular-dynamics potential and carry out large-scale molecular-dynamics calculations. Consequently, we successfully access two kinds of thermodynamic phase transitions, namely the melting and the anomalous λ transition induced by large diffusions of oxygen atoms. Furthermore, we quantitatively reproduce various experimental data in the best agreement manner by selecting a density functional scheme known as SCAN. Our results suggest that the present scale-up simulation-scheme using machine-learning techniques opens up a new pathway on theoretical studies of not only nuclear fuel compounds, but also a variety of similar materials that contain both heavy and light elements, like thorium dioxide.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2026

Machine learning approach for vibronically renormalized electronic band structures

Here, we present a machine learning (ML) method for efficient computation of vibrational thermal expectation values of physical properties from first principles. Our approach is based on the nonperturbative frozen phonon formulation in which stochastic Monte Carlo algorithm is employed to sample configurations of nuclei in a supercell at finite temperatures based on a first-principles phonon model. A deep-learning neural network is trained to accurately predict physical properties associated with sampled phonon configurations, thus bypassing the time-consuming ab initio calculations. To incorporate the point-group symmetry of the electronic system into the ML model, group-theoretical methods are used to develop a symmetry-invariant descriptor for phonon configurations in the supercell. We apply our ML approach to compute the temperature dependent electronic energy gap of silicon based on density functional theory (DFT). We show that, with less than a hundred DFT calculations for training the neural network model, an order of magnitude larger number of sampling can be achieved for the computation of the vibrational thermal expectation values. Our work highlights the promising potential of ML techniques for finite temperature first-principles electronic structure methods.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Atomic scale crystal field mapping of polar vortices in oxide superlattices

Polar vortices in oxide superlattices exhibit complex polarization topologies. Using a combination of electron energy loss near-edge structure analysis, crystal field multiplet theory, and first-principles calculations, we probe the electronic structure within such polar vortices in [(PbTiO 3 ) 16 /(SrTiO 3 ) 16 ] superlattices at the atomic scale. The peaks in Ti L-edge spectra shift systematically depending on the position of the Ti 4+ cations within the vortices i.e., the direction and magnitude of the local dipole. First-principles computation of the local projected density of states on the Ti 3d orbitals, together with the simulated crystal field multiplet spectra derived from first principles are in good agreement with the experiments.

36 MATERIALS SCIENCE↗
NASA NTRS2010

Physics of Life: A Model for Non-Newtonian Properties of Living Systems

This innovation proposes the reconciliation of the evolution of life with the second law of thermodynamics via the introduction of the First Principle for modeling behavior of living systems. The structure of the model is quantum-inspired: it acquires the topology of the Madelung equation in which the quantum potential is replaced with the information potential. As a result, the model captures the most fundamental property of life: the progressive evolution; i.e. the ability to evolve from disorder to order without any external interference. The mathematical structure of the model can be obtained from the Newtonian equations of motion (representing the motor dynamics) coupled with the corresponding Liouville equation (representing the mental dynamics) via information forces. All these specific non-Newtonian properties equip the model with the levels of complexity that matches the complexity of life, and that makes the model applicable for description of behaviors of ecological, social, and economical systems. Rather than addressing the six aspects of life (organization, metabolism, growth, adaptation, response to stimuli, and reproduction), this work focuses only on biosignature ; i.e. the mechanical invariants of life, and in particular, the geometry and kinematics of behavior of living things. Living things obey the First Principles of Newtonian mechanics. One main objective of this model is to extend the First Principles of classical physics to include phenomenological behavior on living systems; to develop a new mathematical formalism within the framework of classical dynamics that would allow one to capture the specific properties of natural or artificial living systems such as formation of the collective mind based upon abstract images of the selves and non-selves; exploitation of this collective mind for communications and predictions of future expected characteristics of evolution; and for making decisions and implementing the corresponding corrections if the expected scenario is different from the originally planned one. This approach postulates that even a primitive living species possesses additional, non-Newtonian properties that are not included in the laws of Newtonian or statistical mechanics. These properties follow from a privileged ability of living systems to possess a self-image (a concept introduced in psychology) and to interact with it. The proposed mathematical system is based on the coupling of the classical dynamical system representing the motor dynamics with the corresponding Liouville equation describing the evolution of initial uncertainties in terms of the probability density and representing the mental dynamics. The coupling is implemented by the information-based supervising forces that can be associated with self-awareness. These forces fundamentally change the pattern of the probability evolution, and therefore, lead to a major departure of the behavior of living systems from the patterns of both Newtonian and statistical mechanics. This innovation is meant to capture the signature of life based only on observable behavior, not on any biochemistry. This will not prevent the use of this model for developing artificial living systems, as well as for studying some general properties of behavior of natural, living systems.

Zak, Michail↗
DOE OSTI2025

Fundamental microscopic properties as predictors of large-scale quantities of interest: Validation through grain boundary energy trends

Correlations between fundamental microscopic properties computable from first principles, which we term canonical properties, and complex large-scale quantities of interest (QoIs) provide an avenue to predictive materials discovery. Here, we propose that such correlations can be efficiently discovered through simulations utilizing approximate interatomic potentials (IPs), which serve as an ensemble of “synthetic materials”. As a proof of principle we build a regression model relating canonical properties to the symmetric tilt grain boundary (GB) energy curves in face-centered cubic crystals, characterized by the scaling factor in the universal lattice matching model of Runnels et al. (2016), which we take to be our QoI. Our analysis recovers known correlations of GB energy to other properties and discovers new ones. We also demonstrate, using available density functional theory (DFT) GB energy data, that the regression model constructed from IP data is consistent with DFT results, confirming the assumption that the IPs and DFT belong to same statistical pool and thereby validating the approach. Regression models constructed in this fashion can be used to predict large-scale QoIs based on first-principles data and provide a general method for training IPs for QoIs beyond the scope of first-principles calculations.

36 MATERIALS SCIENCE↗
NASA NTRS1981

Helicopter rotor trailing edge noise

A two dimensional section of a helicopter main rotor blade was tested in an acoustic wind tunnel at close to full-scale Reynolds numbers to obtain boundary layer data and acoustic data for use in developing an acoustic scaling law and testing a first principles trailing edge noise theory. Results were extended to the rotating frame coordinate system to develop a helicopter rotor trailing edge noise prediction. Comparisons of the calculated noise levels with helicopter flyover spectra demonstrate that trailing edge noise contributes significantly to the total helicopter noise spectrum at high frequencies. This noise mechanism is expected to control the minimum rotor noise. In the case of noise radiation from a local blade segment, the acoustic directivity pattern is predicted by the first principles trailing edge noise theory. Acoustic spectra are predicted by a scaling law which includes Mach number, boundary layer thickness and observer position. Spectrum shape and sound pressure level are also predicted by the first principles theory but the analysis does not predict the Strouhal value identifying the spectrum peak.

Schlinker, R. H.↗
DOE OSTI2025

Boiler Health Monitoring Using a Hybrid First Principles-Artificial Intelligence Model

Due to increased penetration of the intermittent renewables to the grid, pulverized coal (PC) plants are being forced to cycle their load frequently and rapidly, operate at low load condition for sustained period, and start up and shut down several hundred times in a year in the worst case. These severe operations are causing substantial damage to the boiler components compromising the reliability of PC plants. An online health monitoring tool can be instrumental in understanding the impacts of load-following and can eventually help PC plants to develop advanced process control strategies for improved flexibility without compromising safety nor reliability.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

Dependence of predicted bulk properties of hexagonal hydroxyapatite on exchange-correlation functional

Hydroxyapatite (HA) is the main component of human bones and teeth. HA has also been widely applied in various technological fields due to its unique properties. Reliable computational simulations for this critical material often require input parameters obtained from the first-principles density functional theory (DFT) calculations with appropriate exchange–correlation functionals. Previous DFT calculations are insufficient to verify the reliabilities of various functionals, particularly as they fail to assess predictions for multiple properties of the material. In this paper, we first select 18 different functionals to calculate geometric, elastic, electronic, and thermodynamic properties of hexagonal HA bulk crystal. We find that the results from optB86b-vdW and optB88-vdW functionals with dispersion corrections have the overall best agreement with available experimental data. Then, we choose optB88-vdW functional, as well as PBE functional without dispersion corrections as a comparison, to perform extensive first-principles DFT phonon calculations under the quasiharmonic approximation. Various thermodynamic properties (including phonon contributions to internal energy, entropy, and Helmholtz free energy) and thermal parameters (including thermal expansions of volume, thermal expansion coefficients, heat capacities, isothermal bulk moduli, etc.) versus temperature are consequently obtained. By comparing these quantities, we report that the results from optB88-vdW functional have significantly better agreement with available experimental data than those from PBE functional although the latter has been widely used in previous DFT calculations for HA-based material systems.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Search for active and inactive ion insertion sites in organic crystalline materials

The position of mobile active and inactive ions, specifically ion insertion sites, within organic crystals, significantly affects the properties of organic materials used for energy storage and ionic transport. Identifying the positions of these atomic (and ionic) sites in an organic crystal is challenging, especially when the element has low X-ray scattering power, such as lithium (Li) and hydrogen, which are difficult to detect by powder X-ray diffraction. First-principles calculations, exemplified by density functional theory (DFT), are very practical for confirming the relative stability of ion positions in materials. However, the lack of effective strategies to identify ion sites in these organic crystalline frameworks renders this task extremely challenging. This work presents two algorithms: the (i) efficient location of ion insertion sites from extrema in electrostatic local potential and charge density (ELIISE), and the (ii) ElectRostatic InsertioN (ERIN), which leverage charge density and electrostatic potential fields accessed from first-principles calculations, combined with the Simultaneous Ion Insertion and Evaluation (SIIE) workflow. SIIE inserts all ions simultaneously—to determine ion positions in organic crystals. We demonstrate that these methods accurately reproduce known ion positions in 16 organic materials and identify previously overlooked low-energy sites in tetralithium 2,6-naphthalenedicarboxylate (Li4NDC), an organic electrode material, highlighting the importance of inserting all ions simultaneously, as in the SIIE workflow. These algorithms are also integrated with off-the-shelf machine learning potentials, yielding promising results comparable to first-principles findings.

Gopidi, Harshan Reddy [Argonne National Laboratory↗
DOE OSTI2024

Active and Transfer Learning of High-Dimensional Neural Network Potentials for Transition Metals

Classical molecular dynamics (MD) simulations represent a very popular and powerful tool for materials modeling and design. The predictive power of MD hinges on the ability of the interatomic potential to capture the underlying physics and chemistry. There have been decades of seminal work on developing interatomic potentials, albeit with a focus predominantly on capturing the properties of bulk materials. Such physics-based models, while extensively deployed for predicting the dynamics and properties of nanoscale systems over the past two decades, tend to perform poorly in predicting nanoscale potential energy surfaces (PESs) when compared to high-fidelity first-principles calculations. These limitations stem from the lack of flexibility in such models, which rely on a predefined functional form. Machine learning (ML) models and approaches have emerged as a viable alternative to capture the diverse size-dependent cluster geometries, nanoscale dynamics, and the complex nanoscale PESs, without sacrificing the bulk properties. Here, in this study, we introduce an ML workflow that combines transfer and active learning strategies to develop high-dimensional neural networks (NNs) for capturing the cluster and bulk properties for several different transition metals with applications in catalysis, microelectronics, and energy storage, to name a few. Our NN first learns the bulk PES from the high-quality physics-based models in literature and subsequently augments this learning via retraining with a higher-fidelity first-principles training data set to concurrently capture both the nanoscale and bulk PES. Our workflow departs from status-quo in its ability to learn from a sparsely sampled data set that nonetheless covers a diverse range of cluster configurations from near-equilibrium to highly nonequilibrium as well as learning strategies that iteratively improve the fingerprinting depending on model fidelity. All the developed models are rigorously tested against an extensive first-principles data set of energies and forces of cluster configurations as well as several properties of bulk configurations for 10 different transition metals. Our approach is material agnostic and provides a methodology to transfer and build upon the learnings from decades of seminal work in molecular simulations on to a new generation of ML-trained potentials to accelerate materials discovery and design.

36 MATERIALS SCIENCE↗