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At least 343 records · Page 19

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DOE OSTI2021

DMFTwDFT: An open-source code combining Dynamical Mean Field Theory with various density functional theory packages

We report Dynamical Mean Field Theory (DMFT) is a successful method to compute the electronic structure of strongly correlated materials, especially when it is combined with density functional theory (DFT). Here, we present an open-source computational package (and a library) combining DMFT with various DFT codes interfaced through the Wannier90 package. The correlated subspace is expanded as a linear combination of Wannier functions introduced in the DMFT approach as local orbitals. In particular, we provide a library mode for computing the DMFT density matrix. This library can be linked and then internally called from any DFT package, assuming that a set of localized orbitals can be generated in the correlated subspace. The existence of this library allows developers of other DFT codes to interface with our package and achieve the charge-self-consistency within DFT+DMFT loops. To test and check our implementation, we computed the density of states and the band structure of well-known solid-state correlated materials, namely LaNiO, SrVO, and NiO. The obtained results are compared to those obtained from other DFT+DMFT implementations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Subspace recursive Fermi-operator expansion strategies for large-scale DFT eigenvalue problems on HPC architectures

Quantum mechanical calculations for material modeling using Kohn–Sham density functional theory (DFT) involve the solution of a nonlinear eigenvalue problem for N smallest eigenvector-eigenvalue pairs, with N proportional to the number of electrons in the material system. Here, these calculations are computationally demanding and have asymptotic cubic scaling complexity with the number of electrons. Large-scale matrix eigenvalue problems arising from the discretization of the Kohn–Sham DFT equations employing a systematically convergent basis traditionally rely on iterative orthogonal projection methods, which are shown to be computationally efficient and scalable on massively parallel computing architectures. However, as the size of the material system increases, these methods are known to incur dominant computational costs through the Rayleigh–Ritz projection step of the discretized Kohn–Sham Hamiltonian matrix and the subsequent subspace diagonalization of the projected matrix. This work explores the potential of polynomial expansion approaches based on recursive Fermi-operator expansion as an alternative to the subspace diagonalization of the projected Hamiltonian matrix to reduce the computational cost. Subsequently, we perform a detailed comparison of various recursive polynomial expansion approaches to the traditional approach of explicit diagonalization on both multi-node central processing unit and graphics processing unit architectures and assess their relative performance in terms of accuracy, computational efficiency, scaling behavior, and energy efficiency.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Ab initio study on fcc Pr with correlation matrix renormalization theory

Here, we studied fcc praseodymium (Pr) with the ab initio correlation matrix renormalization theory (CMRT) explicitly calculating the Coulomb interactions among basis orbitals and without using any adjustable parameters to work for strongly correlated electron systems. We calculated its total energy in a paramagnetic ground state and studied the role of the correlated 4f electrons in the system. Good agreement was obtained between CMRT and experiments in the pressure volume dependence of the fcc phase. We also compared the CMRT results against other theoretical methods including local density approximation + dynamical mean-field theory and showed consistent results among them. Moreover, we found the normalized local charge fluctuation of the 4f electrons as the function of volume exhibits a clear slope change at the volume collapse region, indicating a switch in their correlation nature there.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Simple and Robust Carrier-Based PWM Technique for Single-Stage Three-phase Rectifier Indirect Matrix Converter

Indirect matrix converters (IMC) can offer high efficiency, high power density, and bidirectional power flow capability with a single-stage three-to single-phase AC-AC power conversion integrating a conventional three-phase active rectifier with a primary side full-bridge inverter of a dual active bridge converter. However, the IMCs generally require space vector modulation techniques, leading to complex sectors and dwell times calculations. A practical implementation of the modulation methods with a commonly used digital signal processor becomes even more challenging when considering power factor correction, harmonics reduction, and deadtime between sector transitions. This paper proposes a simple and robust carrier-based pulse width modulation (PWM) technique determined through a voltage-source inverter (VSI) to current-source inverter (CSI) to IMC conversion. This translation enables the adoption of all conventional VSI control methods with active and reactive power control in the IMCs through simple carrier and reference waveform comparison. The VSI vectors are first one-on-one matched with CSI vectors and translated to IMC modulation signals without calculating sectors and dwell times. Nine different carrier waveforms are proposed, and the converter performance is analyzed. Grid current THD and efficiency are estimated for each modulation method. The symmetric A and B methods had the lowest THD of the grid current, 2.80%, and the efficiency remained at approximately 98.3%, making it the most suitable carrier-based method proposed in this paper. The space vector PWM (SVPWM) modulation method was implemented to the DSP and generated the expected twelve gate signals.

Benson, Mikayla↗

DOE OSTI2024

Interfacial Engineering Using Covalent Organic Frameworks in Polymer Composites for High‐Temperature Electrostatic Energy Storage

Abstract The use of inorganic nanofillers has been an effective method to improve high‐temperature capacitive performance of dielectric polymers, though there are unmet challenges such as undesirable organic–inorganic compatibility, and low efficiencies and energy densities. Herein, a surface functionalization strategy using covalent organic frameworks (COFs) is employed to address such challenges in realizing high‐performing polymer composites. Specifically, core–shell structured nanoparticles, where ZrO 2 nanoparticles act as the core and a COF material forms the shell, are constructed and composited with the polyetherimide (PEI) matrix. The design leverages the high electron affinity ( E A ) of the outer COF shell to create energy traps, thereby capturing free charges and limiting electrical conduction. Concurrently, the low E A and wide bandgap of the ZrO 2 core introduce energy barriers to impede charge injection and migration. This orchestrated “energy level cascade” results in a marked reduction of leakage current and energy loss. The resulting polymer composite showcases an impressive discharged energy density of 6.21 J cm −3 at an efficiency above 90%, with a maximum discharged energy density reaching 7.43 J cm −3 at 150 °C. These performance metrics position the PEI/ZrO 2 @COF polymer composite to surpass or be on par with state‐of‐the‐art high‐temperature PEI composites and other advanced polymer dielectrics.

Xie, Zongliang↗

DOE OSTI2021

Effect of Ligand Polarity on the Internal Dipoles and Ferroelectric Distortion in BaTiO 3 Nanocubes

Here, surface adsorbates and surrounding matrix species have been demonstrated to affect the properties of nanoscale ferroelectrics and nanoscale ferroelectric composites; potentially counteracting performance losses that can occur in small particle sizes. In this work, the effects of nonpolar oleic acid (OA) and polar tetrafluoroborate (BF 4 - ) ligand capping on the surface of various sizes of BaTiO 3 nanocubes have been investigated with combined neutron diffraction and neutron pair distribution function (PDF), density functional theory (DFT), and ab initio molecular dynamics (AIMD) methods. The low real space PDF region provides an unobstructed view of rhombohedral (split short and long) Ti-O distances in BaTiO 3 nanocubes, mimicking the well-established order-disorder local structure found in bulk BaTiO 3 . Interestingly, the intermediate-range order in nanocubes is found to be orthorhombic, rather than tetragonal. It is concluded that polar ligands adsorbed at BaTiO 3 surfaces stabilize the correlation length scale of local rhombohedral distortions in ferroelectric nanoparticles relative to nonpolar ligands.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Data assimilation in operator algebras

We develop an algebraic framework for sequential data assimilation of partially observed dynamical systems. In this framework, Bayesian data assimilation is embedded in a nonabelian operator algebra, which provides a representation of observables by multiplication operators and probability densities by density operators (quantum states). In the algebraic approach, the forecast step of data assimilation is represented by a quantum operation induced by the Koopman operator of the dynamical system. Moreover, the analysis step is described by a quantum effect, which generalizes the Bayesian observational update rule. Projecting this formulation to finite-dimensional matrix algebras leads to computational schemes that are i) automatically positivity-preserving and ii) amenable to consistent data-driven approximation using kernel methods for machine learning. Moreover, these methods are natural candidates for implementation on quantum computers. Applications to the Lorenz 96 multiscale system and the El Niño Southern Oscillation in a climate model show promising results in terms of forecast skill and uncertainty quantification.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Coupled Gel/Seeded Growth of Cs2AgBiBr6 Single Crystals for Radiation Detection

Abstract— The all-inorganic lead free Cs2AgBiBr6 perovskite represents a highly promising environment-friendly radiation detector material. The first attempt to grow Cs2AgBiBr6 single crystals using the unique gel growth method is reported in this study. This technique enables controlled reagent diffusion in a gel medium at room temperature, fostering the growth of visible single crystals. It offers advantages over conventional methods, leading to fewer equilibrium and non-equilibrium defects and simplified execution. Interestingly, the as-grown crystals exhibited variations in size and colors within the gel matrix, indicating the influence of Bi solutes in Bi-poor and Bi-rich Cs2AgBiBr6 crystals. Once the crystals were confirmed by powder xray diffraction, a series of comprehensive material characterizations was performed. The resistivity of Cs2AgBiBr6 single crystals was measured at 1.12x107 Ω•cm. Trap density analysis yielded a value of 2.82 x1010 cm-3. The charge carrier mobility was determined to be 32.9 cm2V-1S-1, suggesting the suitability for radiation detector applications. Additional growth optimization is going on and further characterization will be reported during the presentation. Our work helps accelerate the development of lead-free perovskite materials toward radiation detector applications.

James, Ralph B.↗

DOE OSTI2022

Additive manufacturing of complex objects using refractory matrix materials

A method for the manufacture of a three-dimensional object using a refractory matrix material is provided. The method includes the additive manufacture of a green body from a powder-based refractory matrix material followed by densification via chemical vapor infiltration (CVI). The refractory matrix material can be a refractory ceramic (e.g., silicon carbide, zirconium carbide, or graphite) or a refractory metal (e.g., molybdenum or tungsten). In one embodiment, the matrix material is deposited according to a binder-jet printing process to produce a green body having a complex geometry. The CVI process increases its density, provides a hermetic seal, and yields an object with mechanical integrity. The residual binder content dissociates and is removed from the green body prior to the start of the CVI process as temperatures increase in the CVI reactor. The CVI process selective deposits a fully dense coating on all internal and external surfaces of the finished object.

Terrani, Kurt A.↗

DOE OSTI2024

Additive manufacturing of complex objects using refractory matrix materials

Terrani, Kurt A.↗

DOE OSTI2023

Quantum simulation of exact electron dynamics can be more efficient than classical mean-field methods

Quantum algorithms for simulating electronic ground states are slower than popular classical mean-field algorithms such as Hartree–Fock and density functional theory but offer higher accuracy. Accordingly, quantum computers have been predominantly regarded as competitors to only the most accurate and costly classical methods for treating electron correlation. However, here we tighten bounds showing that certain first-quantized quantum algorithms enable exact time evolution of electronic systems with exponentially less space and polynomially fewer operations in basis set size than conventional real-time time-dependent Hartree–Fock and density functional theory. Although the need to sample observables in the quantum algorithm reduces the speedup, we show that one can estimate all elements of the k-particle reduced density matrix with a number of samples scaling only polylogarithmically in basis set size. We also introduce a more efficient quantum algorithm for first-quantized mean-field state preparation that is likely cheaper than the cost of time evolution. We conclude that quantum speedup is most pronounced for finite-temperature simulations and suggest several practically important electron dynamics problems with potential quantum advantage.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

The Grassmann interpolation method for spin-unrestricted open-shell systems

The recently reported Grassmann interpolation (G-Int) method [J. A. Tan and K. U. Lao, J. Chem. Phys. 158, 051101 (2023)] has been extended to spin-unrestricted open-shell systems. In contrast to closed-shell systems, where G-Int has to be performed only once since the α and β density matrices are the same, spin-unrestricted open-shell systems require G-Int to be performed twice—one for the α spin and another for the β spin density matrix. In this work, we tested the performance of G-Int to the carbon monoxide radical cation CO●+ and nickelocene complex, which have the doublet and triple ground states, respectively. We found that the Frobenius norm errors associated with the interpolations for the α and β spin density matrices are comparable for a given molecular geometry. These G-Int density matrices, when used as an initial guess for a self-consistent field (SCF) calculation, outperform the conventional SCF guess schemes, such as the superposition of atomic densities, purified superposition of atomic densities, core Hamiltonian, and generalized Wolfsberg–Helmholtz approximation. Depending on the desired accuracy, these G-Int density matrices can be used to directly evaluate the SCF energy without performing SCF iterations. In addition, the spin-unrestricted G-Int density matrices have been used for the first time to directly calculate the atomic charges using the Mulliken and ChElPG population analysis.

Chemistry↗

DOE OSTI2020

Basis light-front quantization for a chiral nucleon-pion Lagrangian

We present the first application of the basis light-front quantization method to a simple chiral model of the nucleon-pion system as a relativistic bound state for the physical proton. In this work, the light-front mass-squared matrix of the nucleon-pion system is obtained within a truncated basis. The mass and the corresponding light-front wave function (LFWF) of the proton are computed by numerical diagonalization of the resulting mass-squared matrix. With the boost-invariant LFWF, we calculate the probability density distribution of the pion's longitudinal momentum fraction and the Dirac form factor of the proton.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Fault isolation and fault-tolerant control for Takagi-Sugeno fuzzy time-varying delay stochastic distribution systems

A fault isolation, estimation, and fault-tolerant control (FTC) scheme for nonlinear time-varying delay stochastic distribution control systems was presented in this paper. The Takagi-Sugeno fuzzy model was adopted to approach the nonlinear dynamics of time-varying delay systems. According to the output equivalence principle and Laplace transformation, an augmented state vector was given to solve the time-varying delay problem. When multiple actuator faults and interference occur simultaneously, fault detection, isolation and fault estimation was designed to obtained the fault information. To decouple faults and obtain the value and location information of the fault, the system was separated into two parts through the designed multiple conversion matrices, in which one subsystem was only affected by one actuator fault. This has simplified the design of fault isolation and estimation. A adaptive observer for fault estimation was given. Then, fault information such as the time, location, and size was determined. The observer gain matrices were calculated using linear matrix inequality (LMI). When a fault was detected and diagnosed, a FTC algorithm was devised using the proportional-integral control scheme to compensate the fault as much as possible. It has been shown that even if multiple faults actuator occurred simultaneously, the FTC controller still ensured the output probability density function of the system traced the desired probability density function when a fault occurred. Finally, the expected results were obtained through the simulation example, which confirmed the effectiveness of the method.

42 ENGINEERING↗

DOE OSTI2023

Noise-specific beating in the higher-level Ramsey curves of a transmon qubit

We report: in the higher levels of superconducting transmon devices and more generally charge sensitive devices, T 2 * measurements made in the presence of low-frequency time-correlated 1/f charge noise and quasiparticle-induced parity flips can give an underestimation of the total dephasing time. The charge variations manifest as beating patterns observed in the overlay of several Ramsey fringe curves and are reproduced with a phenomenological Ramsey curve model, which accounts for the charge variations. T 2 * dephasing times, which more accurately represent the total dephasing time, are obtained. The phenomenological model is compared with a Lindblad master equation model. Both models are found to be in agreement with one another and the experimental data. Finally, the phenomenological formulation enables a simple method in which the power spectral density for the low-frequency noise can be inferred from the overlay of several Ramsey curves.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Scalable learning of potentials to predict time-dependent Hartree–Fock dynamics

We propose a framework to learn the time-dependent Hartree–Fock (TDHF) inter-electronic potential of a molecule from its electron density dynamics. Although the entire TDHF Hamiltonian, including the inter-electronic potential, can be computed from first principles, we use this problem as a testbed to develop strategies that can be applied to learn a priori unknown terms that arise in other methods/approaches to quantum dynamics, e.g., emerging problems such as learning exchange–correlation potentials for time-dependent density functional theory. We develop, train, and test three models of the TDHF inter-electronic potential, each parameterized by a four-index tensor of size up to 60 × 60 × 60 × 60. Two of the models preserve Hermitian symmetry, while one model preserves an eight-fold permutation symmetry that implies Hermitian symmetry. Across seven different molecular systems, we find that accounting for the deeper eight-fold symmetry leads to the best-performing model across three metrics: training efficiency, test set predictive power, and direct comparison of true and learned inter-electronic potentials. All three models, when trained on ensembles of field-free trajectories, generate accurate electron dynamics predictions even in a field-on regime that lies outside the training set. To enable our models to scale to large molecular systems, we derive expressions for Jacobian-vector products that enable iterative, matrix-free training.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Ab initio calculation of atomic solid hydrogen phases based on Gutzwiller many-body wave functions

We apply two ab initio many-body methods based on Gutzwiller wave functions, i.e., correlation matrix renormalization theory (CMRT) and Gutzwiller conjugate gradient minimization (GCGM), to the study of crystalline phases of atomic hydrogen. Both methods avoid empirical Hubbard U parameters and are free from double-counting issues. CMRT employs a Gutzwiller-type approximation that enables efficient calculations, while GCGM goes beyond this approximation to achieve higher accuracy at higher computational cost. By benchmarking against available quantum Monte Carlo (QMC) results, we demonstrate that while both methods are more accurate than the widely used density-functional theory, GCGM systematically captures additional correlation energy missing in CMRT, leading to significantly improved total energy predictions. We also show that by including the correlation energy Ec from local density approximation in the CMRT calculation, CMRT + E c produces energy in better agreement with the QMC results in these hydrogen lattice systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

NASMDR: a framework for miRNA-drug resistance prediction using efficient neural architecture search and graph isomorphism networks

Abstract As a frontier field of individualized therapy, microRNA (miRNA) pharmacogenomics facilitates the understanding of different individual responses to certain drugs and provides a reasonable reference for clinical treatment. However, the known drug resistance-associated miRNAs are not yet sufficient to support precision medicine. Although existing methods are effective, they all focus on modelling miRNA-drug resistance interaction graphs, making their performance bounded by the interaction density. In this study, we propose a framework for miRNA-drug resistance prediction through efficient neural architecture search and graph isomorphism networks (NASMDR). NASMDR uses attribute information instead of the commonly used interactive graph information. In the cross-validation experiment, the proposed framework can achieve an AUC of 0.9468 on the ncDR dataset, which is 2.29% higher than the state-of-the-art method. In addition, we propose a novel sequence characterization approach, k-mer Sparse Nonnegative Matrix Factorization (KSNMF). The results show that NASMDR provides novel insights for integrating efficient neural architecture search and graph isomorphic networks into a unified framework to predict drug resistance-related miRNAs. The codes for NASMDR are available at https://github.com/kaizheng-academic/NASMDR.

Zheng, Kai↗