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DOE-STD-1189-2016 Process Standard – Stage Gates Establishment of Regulatory Requirements Clarity of Path Forward Integration of Safety and Design Reduce Project Risk by Establishing Regulatory Certainty through formal regulatory approvals. Flexibility in risk analysis and presentation methods. ANSI/ANS 15.21, DOE-STD-3009, LMP/TICAP
Many materials exhibit phase transitions at which both the electronic properties and the crystal structure change. Some authors have argued that the change in electronic order is primary, with the lattice distortion a relatively minor side-effect, and others have argued that the lattice distortions play an essential role in the energetics of the transition. In this paper, we introduce a formalism that resolves this long-standing problem. The methodology works with any electronic structure method that produces solutions of the equation of state determining the electronic order parameter as a function of lattice distortion. We use the formalism to settle the question of the physics of the metal–insulator transitions in the rare-earth perovskite nickelates (RNiO 3 ) and Ruddlesden–Popper calcium ruthenates (Ca 2 RuO 4 ) in bulk, heterostructure, and epitaxially strained thin film forms, finding that electron-lattice coupling is key to stabilizing the insulating state in both classes of materials.
The total scattering method is the simultaneous study of both the real- and reciprocal-space representations of diffraction data. While conventional Bragg-scattering analysis (employing methods such as Rietveld refinement) provides insight into the average structure of the material, pair distribution function (PDF) analysis allows for a more focused study of the local atomic arrangement of a material. Generically speaking, a PDF is generated by Fourier transforming the total measured reciprocal-space diffraction data (Bragg and diffuse) into a real-space representation. However, the details of the transformation employed and, by consequence, the resultant appearance and weighting of the real-space representation of the system can vary between different research communities. As the worldwide total scattering community continues to grow, these subtle differences in nomenclature and data representation have led to conflicting and confusing descriptions of how the PDF is defined and calculated. This paper provides a consistent derivation of many of these different forms of the PDF and the transformations required to bridge between them. Some general considerations and advice for total scattering practitioners in selecting and defining the appropriate choice of PDF in their own research are presented. This contribution aims to benefit people starting in the field or trying to compare their results with those of other researchers.
We use the supersymmetric formalism to derive an integral formula for the density of states of the Gaussian Orthogonal Ensemble, and then apply saddle-point analysis to give a new derivation of the 1/N-correction to Wigner's law. This extends the work of Disertori on the Gaussian Unitary Ensemble. We also apply our method to the interpolating ensembles of Mehta–Pandey.
In this report Lagrangian analysis of materially conserved scalars is applied to the problem of turbulent transport in variable-density flows. The consequences of an additional material conserved quantity, the density, is generally not acknowledged and leads to significant and meaningfully different expressions for turbulent transport in the moment equations. The formal Lagrangian analysis produces gradient transport expressions substantially different from those obtained by the physically intuitive “argument by analogy” method used in computational models. Various intuitive arguments, in Favre and Reynolds averaged settings, are contrasted to the formal Lagrangian results. Using expressions from the formal analysis, we derive consistent gradient transport closures for the turbulent transport terms that appear in the first- and second-order Favre moment equations. Results for coupled multispecies turbulent transport are given. The analysis is limited to variable-density turbulence in which the dilatation of the fluctuating velocity is small. The results are applicable to turbulent combustion and to stellar convection problems in which the density fluctuations are on the order of the mean density.
A general formalism to compute configurational properties of proteins and other heteropolymers with an arbitrary sequence of charges and non-uniform excluded volume interaction is presented. A variational approach is utilized to predict average distance between any two monomers in the chain. The presented analytical model, for the first time, explicitly incorporates the role of sequence charge distribution to determine relative sizes between two sequences that vary not only in total charge composition but also in charge decoration (even when charge composition is fixed). Furthermore, the formalism is general enough to allow variation in excluded volume interactions between two monomers. Model predictions are benchmarked against the all-atom Monte Carlo studies of Das and Pappu [Proc. Natl. Acad. Sci. U. S. A. 110, 13392 (2013)] for 30 different synthetic sequences of polyampholytes. These sequences possess an equal number of glutamic acid (E) and lysine (K) residues but differ in the patterning within the sequence. Without any fit parameter, the model captures the strong sequence dependence of the simulated values of the radius of gyration with a correlation coefficient of R{sup 2} = 0.9. The model is then applied to real proteins to compare the unfolded state dimensions of 540 orthologous pairs of thermophilic and mesophilic proteins. The excluded volume parameters are assumed similar under denatured conditions, and only electrostatic effects encoded in the sequence are accounted for. With these assumptions, thermophilic proteins are found—with high statistical significance—to have more compact disordered ensemble compared to their mesophilic counterparts. The method presented here, due to its analytical nature, is capable of making such high throughput analysis of multiple proteins and will have broad applications in proteomic studies as well as in other heteropolymeric systems.
In this study, we investigate relativistic spin-1 particles in the V(x,y)=(ω{sub 0}{sup 2}/2)(x{sup 2}+y{sup 2})+k{sub 1}/x{sup 2}+k{sub 2}/y{sup 2} type of Smorodinsky–Winternitz potentials. In the first case, since this Smorodinsky–Winternitz potential has two dimensions, the system was transformed into polar coordinates from Cartesian coordinates. By using Duffin–Kemmer–Petiau formalism with the non-central Smorodinsky–Winternitz potential in two dimensions, the exact bound state energy eigenvalues and corresponding eigenfunctions were determined within the framework of the asymptotic iteration method. Bound state eigenfunctions were obtained in terms of confluent hypergeometric functions. -- Highlights: •We introduce formalism of the DKP equation in two dimensions. •The DKP equation with S–W potential is considered for spin-1 particles. •In order to solve the DKP equation, we explain the asymptotic iteration method (AIM). •Bound state energy eigenvalues and eigenfunctions are obtained by using AIM.
Significance Many-body perturbation theory is one of the pillars of quantum many-body physics and has been used extensively to predict ground-state and excited-state electronic properties of real materials in the past few decades. Nonetheless, few practically used methods in many-body perturbation theory have been justified on a rigorous basis. We present a variational formalism that can be used for the rigorous study of a number of many-body perturbation methods in Euclidean lattice field theory. In particular, this perspective allows us to justify the widely used bold Feynman diagrammatic expansion, without relying on formal arguments such as partial resummation of Feynman diagrams to infinite order.
Zero noise extrapolation (ZNE) is a widely used technique for gate error mitigation on near term quantum computers because it can be implemented in software and does not require knowledge of the quantum computer noise parameters. Traditional ZNE requires a significant resource overhead in terms of quantum operations. A recent proposal using a targeted (or random) instead of fixed identity insertion method (riim versus fiim) requires significantly fewer quantum gates for the same formal precision. We start by showing that riim can allow for ZNE to be deployed on deeper circuits than fiim but requires many more measurements to maintain the same statistical uncertainty. We develop two extensions to fiim and riim. The List Identity Insertion Method (liim) allows to mitigate the error from certain cnot gates, typically those with the largest error. Set Identity Insertion Method (siim) naturally interpolates between the measurement-efficient fiim and the gate-efficient riim allowing to trade off fewer cnot gates for more measurements. Finally, we investigate a way to boost the number of measurements, namely to run ZNE in parallel, utilizing as many quantum devices as are available. We explore the performance of riim in a parallel setting where there is a non-trivial spread in noise across sets of qubits within or across quantum computers.
We extended the fragment molecular orbital (FMO) method interfaced with density functional theory (DFT) into spin unrestricted formalism (UDFT) and developed energy gradients for the ground state and single point excited state energies based on time-dependent DFT. The accuracy of FMO is evaluated in comparison to the full calculations without fragmentation. Electronic excitations in solvated organic radicals and in the blue copper protein, plastocyanin (PDB code: 1BXV), are reported. The contributions of solvent molecules to the electronic excitations are analyzed in terms of the fragment polarization and quantum effects such as interfragment charge transfer.
In this paper, we present the results of systematic test calculations for the electronic structure of thermoelectric material ZnSb using a first-principles full-potential all electron computational method. We used a linear combination of atomic orbitals (LACO) formalism, based on density functional theory (DFT). The exchange-correlation interaction potential of the many electron system was described by using a generalized gradient approximation (GGA). We compared the calculated indirect and direct band gaps as well as the effective masses of holes and electrons in ZnSb with experimental measurement results. The calculated indirect band gap of ZnSb is 0.56 eV, which agrees very well with the experimentally measured values of 0.50 eV ~ 0.61 eV. The calculated direct band gap at X point is 0.89 eV. The calculated effective masses of electrons and holes in ZnSb also agree with experimental data. The systematical test calculations as well as the comparisons of the calculated results with experimental measurements show that the obtained electronic structure of ZnSb would be reliable. We did not observe a major deficiency of the first-principles DFT calculation for the electronic structure of ZnSb, using full-potential all electron LACO method. The reported electronic structure of single crystal ZnSb from this work may provide a fundamental knowledge base for further research and applications for this important thermoelectric material.
Quantum defect embedding theory (QDET) is a many-body embedding method designed to describe condensed systems with strongly correlated electrons localized within a given region of space, for example spin defects in semiconductors and insulators. Although the QDET approach has been successful in predicting the electronic properties of several point defects, several limitations of the method remain. Here, in this work, we propose multiple advances to the QDET formalism. We derive a doublecounting correction that consistently treats the frequency dependence of the screened Coulomb interaction, and we illustrate the effect of including unoccupied orbitals in the active space. In addition, we propose a method to describe hybridization effects between the active space and the environment, and we compare the results of several impurity solvers, providing further insights into improving the reliability and applicability of the method. We present results for defects in diamond and for molecular qubits, including a detailed comparison with experiments.
Atmospheric rivers (ARs) have for the last decade been broadly recognized for their global and regional significance in mediating energy and water cycles. As a distinct large-scale circulation feature, ARs can be characterized by long, narrow bands of atmospheric moisture transport from the tropics to higher latitudes. They are both an important source of water supply to coastal regions as well as a hazard because of their predisposition to increase flood risk. However, predicting ARs poses a significant challenge to the scientific community, as they exhibit large variability from subseasonal-to-interannual time scales. On multi-decadal and longer time scales, it is unclear how the frequency and intensity of ARs will change with a warming climate. Hence ARs play a significant role in understanding predictability of water cycle variability and extremes as well as becoming a factor in assessing risks to future infrastructures. There is a large body of literature that has explored ARs from both global and regional perspectives and from timescales spanning hourly to centennial. Despite significant progress in understanding ARs, significant gaps remain, e.g., in characterizing the dynamical and thermodynamical processes associated with ARs, improving predictions of ARs at weather and subseasonal-to-seasonal timescales, and understanding and quantifying the impacts of ARs on the design and management of built infrastructures. Furthermore, the consensus definition of ARs remains essentially qualitative, and investigators have had to rely on heuristic definitions of ARs for quantitative studies. In the First Atmospheric River Tracking Method Intercomparison Project (ARTMIP) workshop held in May 2017, it was recognized that (1) there are many heuristic AR tracking algorithms employed in the literature, (2) our quantitative—and possibly qualitative— understanding of ARs and their impacts may depend on the details of the algorithm used, (3) no data set yet exists to systematically explore the impact of AR tracking algorithm choice on scientific results, and (4) the broader scientific community therefore lacks any formal guidance on the advantages and disadvantages of different AR tracking methods. The First ARTMIP workshop defined and launched a multi-tiered intercomparison experiment designed to fill this community need. The first tier of ARTMIP was aimed at understanding the impact of AR algorithms on quantitative baseline statistics and characteristics of ARs, and the second tier includes sensitivity studies designed around specific science questions, such as reanalysis uncertainty and influences associated with climate change. The Second ARTMIP workshop was convened on April 23-24, 2018, as a two-day event, and following the completion of Tier 1 of the ARTMIP effort: an experiment in which most existing AR tracking algorithms were run on a standardized data set of reanalyzed weather conditions.
Massive Associative K-biclustering (MAK) is a method to address the key limitations in other biclustering approaches. In a formal evaluation against new simulated data sets with more realistic properties MAK improves recovery of biclusters exhibiting common behavior across conditions, with the highest F1 scores, a measure of a test's accuracy, and lowest false positive rates, the probability of falsely rejecting the null hypothesis, compared to established methods. The developers show how MAK can be applied to a popular data set from Saccharomyces cerevisiae to uncover new properties not captured by other methods. MAK biclusters showed increased statistical enrichments for known regulatory associations and gene functions, including for nearly 30% of the novel MAK biclusters not found by other methods. Compared to other methods, MAK results had at least two-fold higher coverage of differential expression in the overall dataset, an overlapping bicluster network with distinct modules for protein expression and energy metabolism, and biologically relevant overlapping biclusters including cases of combinatorial regulation. MAK is suited for large datasets with many potentially noisy and overlapping patterns of varying sizes.
Causal discovery aims to identify cause-effect mechanisms for better scientific understanding, explainable decision-making, and more accurate modeling. Standard statistical frameworks, such as Granger causality, lack the ability to quantify causal relationships in nonlinear dynamics due to the presence of complex feedback mechanisms, timescale mixing, and nonstationarity. Thus, applying these methods to study causal dynamics in real-world systems, such as the Earth, is a major challenge. Addressing this shortcoming, we leverage deep learning and a Koopman operator-theoretic formalism to present a class of causal discovery algorithms. Kausal uses deep Koopman operator methods to approximate nonlinear dynamics in a linearized vector space in which traditional causal inference methods such as Granger causality can be more easily applied. Our idealized experiments demonstrate Kausal’s superior ability in discovering and characterizing causal signals compared to existing deep learning and non-deep learning state-of-the-art approaches. Finally, the successful identification of major El Niño and La Niña events in observations showcases Kausal’s skill to handle real-world applications.
Purpose: With an MR-linac, radiation is delivered in the presence of a magnetic field. Modifications in the codes of practice (CoPs) for reference dosimetry are required to incorporate the effect of the magnetic field. Methods: In most CoPs the absorbed dose is determined using the well-known kQ formalism as the product of the calibration coefficient, the corrected electrometer reading and kQ, to account for the difference in beam quality. To keep a similar formalism a single correction factor is introduced which replaces kQ, and which corrects for beam quality and B-field, kQ,B. In this study we propose a method to determine kQ,B under reference conditions in the MRLinac without using a primary standard, as the product of:- the ratio between detector readings without and with B-field (kB),- the ratio between doses in the point of measurement with and without B-field (rho),- kQ in the absence of the B-field in the MRLinac beam (kQmrl0,Q0),The ratio of the readings, which covers the change in detector reading due to the different electron trajectories in the detector, was measured with a waterproof ionization chamber (IBA-FC65g) in a water phantom in the MRLinac without and with B-field. The change in dose-to-water in the point of measurement due to the B-field was determined with a Monte Carlo based TPS. Results: For the presented approach, the measured ratio of readings is 0.956, the calculated ratio of doses in the point of measurement is 0.995. Based on TPR20,10 measurements kQ was calculated as 0.989 using NCS-18. This yields a value of 0.9408 for kQ,B. Conclusion: The presented approach to determine kQ,B agrees with a method based on primary standards within 0.4% with an uncertainty of 1% (1 std.uncert). It differs from a similar approach using a PMMA-phantom and an NE2571 chamber with 1.3%.
Auxiliary matrix exponential method is used to derive simple and numerically efficient general expressions for the following, historically rather cumbersome, and hard to compute, theoretical methods: (1) average Hamiltonian theory following interaction representation transformations; (2) Bloch-Redfield-Wangsness theory of nuclear and electron relaxation; (3) gradient ascent pulse engineering version of quantum optimal control theory. In the context of spin dynamics, the auxiliary matrix exponential method is more efficient than methods based on matrix factorizations and also exhibits more favourable complexity scaling with the dimension of the Hamiltonian matrix.
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