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DOE OSTI2014

Contribution of the entropy on the thermodynamic equilibrium of vacancies in nickel

The equilibrium vacancy concentration in nickel was determined from ab initio calculations performed with both generalized gradient approximation and local density approximation up to the melting point. We focus the study on the vacancy formation entropy expressed as a sum of a vibration and an electronic contribution, which were determined from the vibration modes and the electronic densities of states. Applying a method based on the quasi-harmonic approximation, the temperature dependence of the defect formation energy and entropy were calculated. We show that the vibrations of the first shell of atoms around the defect are predominant to the vibration formation entropy. On the other hand, the electronic formation entropy is very sensitive to the exchange-correlation potential used for the calculations. Finally, the vacancy concentration is computed at finite temperature with the calculated values for the defect formation energy and entropy. In order to reconcile point-defects concentration obtained with our calculations and experimental data, we conducted complementary calorimetric measurements of the vacancy concentration in the 1073–1273 K temperature range. Close agreement between theory and experiments at high temperature is achieved if the calculations are performed with the generalized gradient approximation and both vibration and electronic contributions to the formation entropy are taken into account.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

An extrapolation method to remove spurious stress concentration in micromechanical analyses of composites using pixel-based meshes

It is easier to generate a finite element mesh of complicated geometries with voxel-based meshes compared to conformal meshes. However, the local stresses in voxel-based meshes are higher owing to artificial stress concentration caused due to jagged lines that approximate a curve. Here, we propose a method that accounts for this anomaly which would lead to accurate prediction of maximum von Mises stress in voxel-based meshes. The method is based on the variation of reciprocal of stresses and associated numerical gradient. The prediction of the proposed method is compared against the results of a conformal mesh by considering four two-dimensional example problems. The accuracy of the proposed method is verified using different mesh densities, material properties, and integration schemes. The proposed method was also implemented on a conformal mesh to verify that it filters out just the spurious stress concentration and not the realistic stress concentration.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Fragme∩t: An Open‐Source Framework for Multiscale Quantum Chemistry Based on Fragmentation

Fragment-based quantum chemistry offers a means to circumvent the nonlinear computational scaling of conventional electronic structure calculations, by partitioning a large calculation into smaller subsystems then considering the many-body interactions between them. Variants of this approach have been used to parameterize classical force fields and machine learning potentials, applications that benefit from interoperability between quantum chemistry codes. However, there is a dearth of software that provides interoperability yet is purpose-built to handle the combinatorial complexity of fragment-based calculations. To fill this void we introduce “Fragme∩t”, an open-source software application that provides a tool for community validation of fragment-based methods, a platform for developing new approximations, and a framework for analyzing many-body interactions. Fragme∩t includes algorithms for automatic fragment generation and structure modification, and for distance- and energy-based screening of the requisite subsystems. Checkpointing, database management, and parallelization are handled internally and results are archived in a portable database. Interfaces to various quantum chemistry engines are easy to write and exist already for Q-Chem, PySCF, xTB, Orca, CP2K, MRCC, Psi4, NWChem, GAMESS, and MOPAC. Applications reported here demonstrate parallel efficiencies around 96% on more than 1000 processors but also showcase that the code can handle large-scale protein fragmentation using only workstation hardware, all with a codebase that is designed to be usable by non-experts. Fragme∩t conforms to modern software engineering best practices and is built upon well established technologies including Python, SQLite, and Ray. The source code is available under the Apache 2.0 license.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Effect of Tuning the Structure on the Optical and Magnetic Properties by Various Transition Metal Doping in ZnO/TM (TM = Fe, FeCo, Cr, and Mn) Thin Films

We have investigated in this work the physical, optical, electronic, and the magnetic behavior (Curie temperature, magnetic moment) of Zn.1-x.MxO (M = Fe 5%, Co 1%, Cr 5%, and Mn 5%), diluted magnetic semiconductors (DMSs). The samples were deposited on glass substrate by the spray pyrolysis technique, and the results of the x-ray diffraction (XRD) of the prepared substrates was used to prove the incorporation of the dopants into the ZnO lattice host; the ferromagnetic and the antiferromagnetic state competitions and their effects on the physical, magnetic, and optical properties, were investigated. The electronic structure and magnetic properties of transition metal (TM) defects, were investigated in detail, by using the Korringa–Kohn–Rostoker (KKR) method combined with the coherent potential approximation (CPA). As a result, doping by TM impurities induce the ferromagnetism with different competitions between the ferromagnetic and antiferromagnetic states, which affects the physical properties of the TM (Fe, Fe/Co, Cr, and Mn)-doped ZnO, with a Curie temperature closer to room-temperature ferromagnetic.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2019

Half Metallic Ferromagnetic Character in ZnXP{sub 2} (X = Ge, Si) Chalcopyrites Doped with Mn

The electronic and the magnetic properties of ZnXP{sub 2} (X = Ge, Si) were studied using the Korring-Kohn-Rostoker (KKR) method combined with the coherent potential approximation (CPA). The total and partial density of state (DOS) are computed for different Mn concentrations. The total magnetic moment, FM and DLM energies, and their variation as well as Curie temperature are also given. It is shown that the substitution of X cations (X = Ge, Si) by Mn atoms in ZnXP{sub 2} chalcopyrites leads to half metallic ferromagnetic character with double exchange mechanism. Thus, the critical temperature can be controlled by varying the concentration of manganese impurity.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Pareto-Lognormal Modeling of Known and Unknown Metal Resources. II. Method Refinement and Further Applications

Pareto-lognormal modeling of worldwide metal deposit size–frequency distributions was proposed in an earlier paper (Agterberg in Nat Resour 26:3–20, 2017). In the current paper, the approach is applied to four metals (Cu, Zn, Au and Ag) and a number of model improvements are described and illustrated in detail for copper and gold. The new approach has become possible because of the very large inventory of worldwide metal deposit data recently published by Patiño Douce (Nat Resour 25:97–124, 2016c). Worldwide metal deposits for Cu, Zn and Ag follow basic lognormal size–frequency distributions that form straight lines on lognormal Q–Q plots. Au deposits show a departure from the straight-line model in the vicinity of their median size. Both largest and smallest deposits for the four metals taken as examples exhibit hyperbolic size–frequency relations and their Pareto coefficients are determined by fitting straight lines on log rank–log size plots. As originally pointed out by Patiño Douce (Nat Resour Res 25:365–387, 2016d), the upper Pareto tail cannot be distinguished clearly from the tail of what would be a secondary lognormal distribution. The method previously used in Agterberg (2017) for fitting the bridge function separating the largest deposit size–frequency Pareto tail from the basic lognormal is significantly improved in this paper. A new method is presented for estimating the approximate deposit size value at which the upper tail Pareto comes into effect. Although a theoretical explanation of the proposed Pareto-lognormal distribution model is not a required condition for its applicability, it is shown that existing double Pareto-lognormal models based on Brownian motion generalizations of the multiplicative central limit theorem are not applicable to worldwide metal deposits. Neither are various upper tail frequency amplification models in their present form. Although a physicochemical explanation remains possible, it is argued that preferential mining of the largest and smallest orebodies can have economic historical reasons. The size–frequency distribution of uranium can be regarded as lognormal without Pareto tails. At the end of the paper, it is shown that original copper deposit size data can be used for forward projection of discovery trends toward the end of this century.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Optoelectronic properties of germanium iodide perovskites AGeI{sub 3} (A = K, Rb and Cs): first principles investigations

In this paper, we have investigated the structural, optoelectronic and elastic properties of AGeI{sub 3} (A = K, Rb and Cs) using the density functional theory with generalized gradient approximation (GGA) for potential exchange correlation. The modified Becke–Johnson (mBJ-GGA) potential approximation is also used for calculating the optoelectronic properties of the material. The results show that the band structure of the perovskites AGeI{sub 3} (have a semiconductor behavior with direct band gap at R–R direction, the gap energy values calculated with mBJ-GGA, for each compound as following: 0.58, 0.63, and 0.71 eV, respectively. The optical properties, such as real and imaginary parts of the dielectric functions, refractive index, reflectivity, conductivity and absorption coefficient are investigated. As results, these compounds are competent candidates photovoltaic application like light harvester.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Thermodynamically consistent algorithms for models of incompressible multiphase polymer solutions with a variable mobility

Here we present a general strategy for developing structure and property preserving numerical algorithms for thermodynamically consistent models of incompressible multiphase polymer solutions with a variable mobility. We first present a formalism to derive thermodynamically consistent, incompressible, multiphase polymer models. Then, we develop the general strategy, known as the supplementary variable method, to devise thermodynamically consistent numerical approximations to the models. We illustrate the numerical strategy using newly developed models of incompressible diblock copolymer solutions coupled with an electric and a magnetic field, respectively. Mesh refinement is conducted to verify convergence rates of the developed schemes. Some numerical examples are given to exhibit underlying dynamics absent from and driven by the external fields, respectively, highlighting differences between models with the variable and constant mobilities.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

A benchmark of Gutzwiller conjugate gradient minimization method in ground state energy calculations of dimers

Herein we present numerical results of ground-state energies of 9 molecules in the well-established G2 molecule set given by the Gutzwiller conjugate gradient minimization (GCGM) method. The method, beyond the commonly used Gutzwiller approximation, was recently developed based on Gutzwiller variational wave functions. We find that compared to benchmark data given by full configuration interaction, GCGM total energies are reasonably well reproduced with the minimum basis set. To include the dynamical correlation beyond the minimal basis calculations, we adopt the local density approximation for the dynamical correlation energy $E_c$. By comparing the results with benchmark data given by experiments and large-basis configuration interaction, the GCGM total energies with $E_c$ are in general better reproduced, but discrepancies are still observed for some dimers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Fantômas unconfined: global QCD fits with Bézier parameterizations

Fantômas is a C++ toolkit for exploring the parametrization dependence of parton distribution functions (PDFs) and other correlator functions in quantum chromodynamics (QCD). Fantômas facilitates the generation of adaptable polynomial parametrizations for PDFs, called metamorphs, to find best-fit PDF solutions and quantify the epistemic uncertainty associated with the parametrizations during their fitting. The method employs Bézier curves as universal approximators for a variety of PDF shapes. Integrated into the xFitter framework for the global QCD analysis, Fantômas provides a foundation for general models of PDFs, while reducing the computational time compared to the approaches utilizing traditional polynomial parametrizations as well as providing an interpretable alternative to neural-network-based models. This paper outlines the structure and practical usage of the Fantômas toolkit, including its inputs, outputs, and implementation within xFitter. It also provides a practical example of using Fantômas for uncertainty quantification as well as the combination of PDF fits into a single ensemble.

Bézier curves↗

DOE OSTI2015

Scalable and efficient algorithms for the propagation of uncertainty from data through inference to prediction for large-scale problems, with application to flow of the Antarctic ice sheet

The majority of research on efficient and scalable algorithms in computational science and engineering has focused on the forward problem : given parameter inputs, solve the governing equations to determine output quantities of interest. In contrast, in this paper, we consider the broader question: given a (large-scale) model containing uncertain parameters, (possibly) noisy observational data, and a prediction quantity of interest, how do we construct efficient and scalable algorithms to (1) infer the model parameters from the data (the deterministic inverse problem ), (2) quantify the uncertainty in the inferred parameters (the Bayesian inference problem), and (3) propagate the resulting uncertain parameters through the model to issue predictions with quantified uncertainties (the forward uncertainty propagation problem )? We present efficient and scalable algorithms for this end-to-end, data-to-prediction process under the Gaussian approximation and in the context of modeling the flow of the Antarctic ice sheet and its effect on loss of grounded ice to the ocean. The ice is modeled as a viscous, incompressible, creeping, shear-thinning fluid. The observational data come from satellite measurements of surface ice flow velocity, and the uncertain parameter field to be inferred is the basal sliding parameter, represented by a heterogeneous coefficient in a Robin boundary condition at the base of the ice sheet. The prediction quantity of interest is the present-day ice mass flux from the Antarctic continent to the ocean. We show that the work required for executing this data-to-prediction process—measured in number of forward (and adjoint) ice sheet model solves—is independent of the state dimension, parameter dimension, data dimension, and the number of processor cores. The key to achieving this dimension independence is to exploit the fact that, despite their large size, the observational data typically provide only sparse information on model parameters. This property can be exploited to construct a low rank approximation of the linearized parameter-to-observable map via randomized SVD methods and adjoint-based actions of Hessians of the data misfit functional.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Variational Bayesian strategies for high-dimensional, stochastic design problems

This paper is concerned with a lesser-studied problem in the context of model-based, uncertainty quantification (UQ), that of optimization/design/control under uncertainty. The solution of such problems is hindered not only by the usual difficulties encountered in UQ tasks (e.g. the high computational cost of each forward simulation, the large number of random variables) but also by the need to solve a nonlinear optimization problem involving large numbers of design variables and potentially constraints. We propose a framework that is suitable for a class of such problems and is based on the idea of recasting them as probabilistic inference tasks. To that end, we propose a Variational Bayesian (VB) formulation and an iterative VB–Expectation-Maximization scheme that is capable of identifying a local maximum as well as a low-dimensional set of directions in the design space, along which, the objective exhibits the largest sensitivity. We demonstrate the validity of the proposed approach in the context of two numerical examples involving thousands of random and design variables. In all cases considered the cost of the computations in terms of calls to the forward model was of the order of 100 or less. The accuracy of the approximations provided is assessed by information-theoretic metrics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Deep learning of free boundary and Stefan problems

Free boundary problems appear naturally in numerous areas of mathematics, science and engineering. These problems present a great computational challenge because they necessitate numerical methods that can yield an accurate approximation of free boundaries and complex dynamic interfaces. In this work, we propose a multi-network model based on physics-informed neural networks to tackle a general class of forward and inverse free boundary problems called Stefan problems. Specifically, we approximate the unknown solution as well as any moving boundaries by two deep neural networks. Besides, we formulate a new type of inverse Stefan problems that aim to reconstruct the solution and free boundaries directly from sparse and noisy measurements. We demonstrate the effectiveness of our approach in a series of benchmarks spanning different types of Stefan problems, and illustrate how the proposed framework can accurately recover solutions of partial differential equations with moving boundaries and dynamic interfaces. All code and data accompanying this manuscript are publicly available at https://github.com/PredictiveIntelligenceLab/DeepStefan.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Kernel learning backward SDE filter for data assimilation

In this paper, we develop a kernel learning backward SDE filter method to estimate the state of a stochastic dynamical system based on its partial noisy observations. A system of forward backward stochastic differential equations is used to propagate the state of the target dynamical model, and Bayesian inference is applied to incorporate the observational information. Further, to characterize the dynamical model in the entire state space, we introduce a kernel learning method to learn a continuous global approximation for the conditional probability density function of the target state by using discrete approximated density values as training data. Numerical experiments demonstrate that the kernel learning backward SDE is highly effective.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

High-order limiting methods using maximum principle bounds derived from the Boltzmann equation I: Euler equations

The use of limiting methods for high-order numerical approximations of hyperbolic conservation laws generally requires defining an admissible region/bounds for the solution. In this work, we present a novel approach for computing solution bounds and limiting for the Euler equations through the kinetic representation provided by the Boltzmann equation, which allows for extending limiters designed for linear advection directly to the Euler equations. Given an arbitrary set of solution values to compute bounds over (e.g., numerical stencil) and a desired linear advection limiter, the proposed approach yields an analytic expression for the admissible region of particle distribution function values, which may be numerically integrated to yield a set of bounds for the density, momentum, and total energy. Further, these solution bounds are shown to preserve positivity of density/pressure/internal energy and, when paired with a limiting technique, can robustly resolve strong discontinuities while recovering high-order accuracy in smooth regions without any ad hoc corrections (e.g., relaxing the bounds). This approach is demonstrated in the context of an explicit unstructured high-order discontinuous Galerkin/flux reconstruction scheme for a variety of difficult problems in gas dynamics, including cases with extreme shocks and shock-vortex interactions. Furthermore, this work presents a foundation for limiting techniques for more complex macroscopic governing equations that can be derived from an underlying kinetic representation for which admissible solution bounds are not well-understood.

42 ENGINEERING↗

DOE OSTI2012

First-principles studies of Ni-Ta intermetallic compounds

The structural properties, heats of formation, elastic properties, and electronic structures of Ni-Ta intermetallic compounds are investigated in detail based on density functional theory. Our results indicate that all Ni-Ta intermetallic compounds calculated here are mechanically stable except for P21/m-Ni{sub 3}Ta and hc-NiTa{sub 2}. Furthermore, we found that Pmmn-Ni{sub 3}Ta is the ground state stable phase of Ni{sub 3}Ta polymorphs. The polycrystalline elastic modulus has been deduced by using the Voigt-Reuss-Hill approximation. All Ni-Ta intermetallic compounds in our study, except for NiTa, are ductile materials by corresponding G/K values and poisson's ratio. The calculated heats of formation demonstrated that Ni{sub 2}Ta are thermodynamically unstable. Our results also indicated that all Ni-Ta intermetallic compounds analyzed here are conductors. The density of state demonstrated the structure stability increases with the Ta concentration. - Graphical abstract: Mechanical properties and formation heats of Ni-Ta intermetallic compounds are discussed in detail in this paper. Highlights: Black-Right-Pointing-Pointer Ni-Ta intermetallic compounds are investigated by first principle calculations. Black-Right-Pointing-Pointer P21/m-Ni{sub 3}Ta and hc-NiTa{sub 2} are mechanically unstable phases. Black-Right-Pointing-Pointer Pmmn-Ni{sub 3}Ta is ground stable phase of Ni{sub 3}Ta polymorphs. Black-Right-Pointing-Pointer All Ni-Ta intermetallic compounds are conducting materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2013

Ab initio density functional theory investigation of the structural, electronic and optical properties of Ca{sub 3}Sb{sub 2} in hexagonal and cubic phases

A density functional theory study of structural, electronical and optical properties of Ca{sub 3}Sb{sub 2} compound in hexagonal and cubic phases is presented. In the exchange–correlation potential, generalized gradient approximation (PBE-GGA) has been used to calculate lattice parameters, bulk modulus, cohesive energy, dielectric function and energy loss spectra. The electronic band structure of this compound has been calculated using the above two approximations as well as another form of PBE-GGA, proposed by Engle and Vosko (EV-GGA). It is found that the hexagonal phase of Ca{sub 3}Sb{sub 2} has an indirect gap in the Γ→N direction; while in the cubic phase there is a direct-gap at the Γ point in the PBE-GGA and EV-GGA. Effects of applying pressure on the band structure of the system studied and optical properties of these systems were calculated. - Graphical abstract: A density functional theory study of structural, electronic and optical properties of Ca{sub 3}Sb{sub 2} compound in hexagonal and cubic phases is presented. Display Omitted - Highlights: • Physical properties of Ca{sub 3}Sb{sub 2} in hexagonal and cubic phases are investigated. • It is found that the hexagonal phase is an indirect gap semiconductor. • Ca{sub 3}Sb{sub 2} is a direct-gap semiconductor at the Γ point in the cubic phase. • By increasing pressure the semiconducting band gap and anti-symmetry gap are decreased.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Uncertainty guided online ensemble for non-stationary data streams in fusion science

Machine Learning (ML) is poised to play a pivotal role in the development and operation of next-generation fusion devices. Fusion data shows non-stationary behavior with distribution drifts, resulted by both experimental evolution and machine wear-and-tear. ML models assume stationary distribution and fail to maintain performance when encountered with such non-stationary data streams. Online learning techniques have been leveraged in other domains, however it has been largely unexplored for fusion applications. In this paper, we investigate online learning for continuous adaptation to drifting data streams in the prediction of Toroidal Field (TF) coils deflection at the DIII-D fusion facility. We further address the short-term performance degradation inherent to standard online learning, which arises because ground truth is unavailable at prediction time. To mitigate this issue, we propose an uncertainty-guided online ensemble framework. The method leverages the Deep Gaussian Process Approximation (DGPA) for calibrated uncertainty estimation and uses these uncertainty measures to guide a meta-algorithm that aggregates predictions from learners trained over different historical horizons. Our results show that online learning reduces prediction error by 80% compared to a static model. The online ensemble and the proposed uncertainty-guided ensemble further reduce error by approximately 6%, and 10% respectively, relative to standard single-model online learning, while also providing calibrated uncertainty estimates to support operational decision-making.

AI↗