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Performance of wave function and Green's function methods for non-equilibrium many-body dynamics
Theoretical descriptions of the non-equilibrium dynamics of quantum many-body systems essentially employ either (i) explicit treatments, relying on the truncation of the expansion of the many-body wave function, (ii) compressed representations of the many-body wave function, or (iii) evolution of an effective (downfolded) representation through Green's functions. In this work, we select representative cases of each of the methods and address how these complementary approaches capture the dynamics driven by intense field perturbations to non-equilibrium states. Under strong driving, the systems are characterized by strong entanglement of the single-particle density matrix and natural populations approaching those of a strongly interacting equilibrium system. We generate a representative set of results that are numerically exact and form a basis for a critical comparison of the distinct families of methods. We demonstrate that the compressed formulation based on similarity-transformed Hamiltonians (coupled-cluster approach) is practically exact in weak fields and, hence, weakly or moderately correlated systems. Coupled cluster, however, struggles for strong driving fields, under which the system exhibits strongly correlated behavior, as measured by the von Neumann entropy of the single-particle density matrix. The dynamics predicted by Green's functions in the (widely popular) G W approximation are less accurate, but improve significantly upon the mean-field results in the strongly driven regime. Published by the American Physical Society 2025
Impacts of Voltage-Based Grid Support Functions on Energy Production of PV Customers
This paper presents the impact of inverter grid- support functions (GSFs) on photovoltaic (PV) customer energy production on a real distribution feeder in Oahu, HI. These autonomous GSFs based on local voltage measurements are good alternatives to increase PV hosting capacity. However, these functions can result in PV energy curtailment to the customer, and this study addresses the concerns about the impact of inverter GSFs through detailed quasi-static time series (QSTS) simulations. It proposes four metrics: maximum and average GSF curtailment, average increased generation and average net generation change, to assess the impact of a given control on PV systems located on customer sites. It was found that curtailment of PV production is negligible for customers where peak voltage is within ANSI C84.1 range. A reliable relationship between curtailment and peak customer voltage is demonstrated, suggesting that peak voltage could be used as an indicator of customer curtailment.
Cellulose synthase ‘class specific regions’ are intrinsically disordered and functionally undifferentiated: Intrinsic disorder in cellulose synthases
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Structure and function of LCI1: a plasma membrane CO[subscript 2] channel in the Chlamydomonas CO[subscript 2] concentrating mechanism
Abstract not provided
Full momentum- and energy-resolved spectral function of a 2D electronic system
Not provided.
Structural, Functional, and Immunogenic Insights on Cu,Zn Superoxide Dismutase Pathogenic Virulence Factors from Neisseria meningitidis and Brucella abortus
ABSTRACT Bacterial pathogens Neisseria meningitidis and Brucella abortus pose threats to human and animal health worldwide, causing meningococcal disease and brucellosis, respectively. Mortality from acute N. meningitidis infections remains high despite antibiotics, and brucellosis presents alimentary and health consequences. Superoxide dismutases are master regulators of reactive oxygen and general pathogenicity factors and are therefore therapeutic targets. Cu,Zn superoxide dismutases (SODs) localized to the periplasm promote survival by detoxifying superoxide radicals generated by major host antimicrobial immune responses. We discovered that passive immunization with an antibody directed at N. meningitidis SOD (NmSOD) was protective in a mouse infection model. To define the relevant atomic details and solution assembly states of this important virulence factor, we report high-resolution and X-ray scattering analyses of NmSOD and of SOD from B. abortus (BaSOD). The NmSOD structures revealed an auxiliary tetrahedral Cu-binding site bridging the dimer interface; mutational analyses suggested that this metal site contributes to protein stability, with implications for bacterial defense mechanisms. Biochemical and structural analyses informed us about electrostatic substrate guidance, dimer assembly, and an exposed C-terminal epitope in the NmSOD dimer. In contrast, the monomeric BaSOD structure provided insights for extending immunogenic peptide epitopes derived from the protein. These collective results reveal unique contributions of SOD to pathogenic virulence, refine predictive motifs for distinguishing SOD classes, and suggest general targets for antibacterial immune responses. The identified functional contributions, motifs, and targets distinguishing bacterial and eukaryotic SOD assemblies presented here provide a foundation for efforts to develop SOD-specific inhibitors of or vaccines against these harmful pathogens. IMPORTANCE By protecting microbes against reactive oxygen insults, SODs aid survival of many bacteria within their hosts. Despite the ubiquity and conservation of these key enzymes, notable species-specific differences relevant to pathogenesis remain undefined. To probe mechanisms that govern the functioning of Neisseria meningitidis and Brucella abortus SODs, we used X-ray structures, enzymology, modeling, and murine infection experiments. We identified virulence determinants common to the two homologs, assembly differences, and a unique metal reservoir within meningococcal SOD that stabilizes the enzyme and may provide a safeguard against copper toxicity. The insights reported here provide a rationale and a basis for SOD-specific drug design and an extension of immunogen design to target two important pathogens that continue to pose global health threats.
Analysis of Electric Vehicle Charging Behavior Patterns with Function Principal Component Analysis Approach
This manuscript focused on analyzing electric vehicles’ (EV) charging behavior patterns with a functional data analysis (FDA) approach, with the goal of providing theoretical support to the EV infrastructure planning and regulation, as well as the power grid load management. 5-year real-world charging log data from a total of 455 charging stations in Kansas City, Missouri, was used. The focuses were placed on analyzing the daily usage occupancy variability, daily energy consumption variability, and station-level usage variability. Compared with the traditional discrete-based analysis models, the proposed FDA modeling approach had unique advantages in preserving the smooth function behavior of the data, bringing more flexibility in the modeling process with little required assumptions or background knowledge on independent variables, as well as the capability of handling time series data with different lengths or sizes. In addition to the patterns revealed in the EV charging station’s occupancy and energy consumption, the differences between EV driver’s charging time and parking time were analyzed and called for the needs for parking regulation and enforcement. The different usage patterns observed at charging stations located on different land-use types were also analyzed.
Development of combined function dipole-quadrupole PMQs magnets for NSLS-II upgrade
This paper focuses on the R&D performed for the development of permanent magnets-based dipoles-quadrupoles combined function magnets (PMQs) for the future NSLSII upgrade “complex bend” lattice (CB). This new lattice uses PMQs that provide both bending (dipole) and strong focusing (quadrupole) magnetic field on the electron beam. The permanent magnet (PM) technology is suitable for the high magnetic field strengths (0.5 T, 130 T/m) required for such combine function magnets. PM technology leads to a compact magnet design that is essential in realizing the complex bend lattice concept, as well as a passive magnet solution which does not require electrical power supply reducing power consumption by ~ 80% (from 1.7 MW to 0.3 MW for NSLS-II). Two PMQs magnets designs are under consideration: A hybrid design that use both PM and soft iron poles, and Halbach type that is a pure PM design. Both PMQs designs present challenges in achieving the specified magnetic field quality due to their higher sensitivity to errors (mechanical tolerances and PM properties). This paper presents cost-effective designs and prototypes results for hybrid and Halbach PMQs, addressing various technical challenges while meeting the field requirements of the complex bend lattice for the NSLS-II upgrade.
Functional Characterization of Four Putative δ1-Pyrroline-5-Carboxylate Reductases from Bacillus subtilis
In most living organisms, the amino acid proline is synthesized starting from both glutamate and ornithine. In prokaryotes, in the absence of an ornithine cyclodeaminase that has been identified to date only in a small number of soil and plant bacteria, these pathways share the last step, the reduction of δ1-pyrroline-5-carboxylate (P5C) catalyzed by P5C reductase (EC 1.5.1.2). In several species, multiple forms of P5C reductase have been reported, possibly reflecting the dual function of proline. Aside from its common role as a building block of proteins, proline is indeed also involved in the cellular response to osmotic and oxidative stress conditions. Genome analysis of Bacillus subtilis identifies the presence of four genes (ProH, ProI, ProG, and ComER) that, based on bioinformatic and phylogenic studies, were defined as respectively coding a putative P5C reductase. Here we describe the cloning, heterologous expression, functional analysis and small-angle X-ray scattering studies of the four affinity-purified proteins. Results showed that two of them, namely ProI and ComER, lost their catalytic efficiency or underwent subfunctionalization. In the case of ComER, this could be likely explained by the loss of the ability to form a dimer, which has been previously shown to be an essential structural feature of the catalytically active P5C reductase. The properties of the two active enzymes are consistent with a constitutive role for ProG, and suggest that ProH expression may be beneficial to satisfy an increased need for proline.
Systematic theoretical investigation of the zero-field splitting in Gd(III) complexes: Wave function and density functional approaches
The zero-field splitting (ZFS) of the electronic ground state in paramagnetic ions is a sensitive probe of the variations in the electronic and molecular structure with an impact on fields ranging from fundamental physical chemistry to medical applications. A detailed analysis of the ZFS in a series of symmetric Gd(III) complexes is presented in order to establish the applicability and accuracy of computational methods using multiconfigurational complete-active-space self-consistent field wave functions and of density functional theory calculations. The various computational schemes are then applied to larger complexes Gd(III)DOTA(H{sub 2}O){sup −}, Gd(III)DTPA(H{sub 2}O){sup 2−}, and Gd(III)(H{sub 2}O){sub 8}{sup 3+} in order to analyze how the theoretical results compare to experimentally derived parameters. In contrast to approximations based on density functional theory, the multiconfigurational methods produce results for the ZFS of Gd(III) complexes on the correct order of magnitude.
Development of Application Function Blocks for Power-Hardware-in-the-Loop Testing of Grid-Connected Inverters: Preprint
Stability and accuracy of the PHIL platform has been a common problem and challenging issue for a long time, thus continuous efforts and research have been devoted to developing interface algorithms to improve upon this. Some of existing works exhibit promising solutions to compensate the errors in the PHIL platform. However, there is a need to harmonize various solutions into a standard framework that provides a general solution for various PHIL test beds and that can be reused with minimum modifications to avoid extra effort. A good way to do this is by using application function blocks (AFBs), which are model-based and can be developed as a standard library component, thus achieving satisfactory reusability and reconfigurability for various platforms. Since PHIL simulation has the flexibility of the software simulation, proper functions can be easily implemented in the digital real-time simulator (DRTS) to preprocess the reference signal before sending it to the power amplifier. Therefore, this paper aims to develop standard AFBs in DRTS for the PHIL interface to test grid-connected inverters. The PHIL interface algorithm is separated into five AFBs that will be presented in detail in this paper.
Structural- and Functional-Informed Machine Learning for Protein Function Prediction
In this project we aimed to extend methods for protein function prediction to include structural prediction data, and benchmark methods against existing tools. We proposed to apply the method to large metagenome datasets, and develop approaches to examine activity-based protein profiling results for protein function-structure patterns. Nitrogen cycle protein families were previously identified and are used here to provide a proof-of-principle for use of structure prediction in protein function classification.
Functional approach to exploring climatic and landscape controls on runoff generation: 2 Timing of runoff storm response
Abstract Hortonian overland flow, Dunne overland flow, and subsurface stormflow are the three most dominant mechanisms contributing to both the volume and timing of streamflow in headwater catchments. In this paper, guided by the Dunne diagram, we explore the impacts of climate, soil, and topography on estimated probability distributions of the travel times of each of these three runoff components. In each case, these are expressed in terms of the Connected Instantaneous Response Functions (CIRF) and account for the dynamics of their individual partial effective contributing areas that retain the connectivity to the outlet (instead of the whole catchment area). A spatially distributed hydrological model is used to derive the CIRFs numerically under multiple combinations of climate, soil, and topographic properties. The mean travel times and dimensionless forms of the CIRFs (i.e., scaled by their respective mean travel times) are used to examine both advective and dispersive aspects of catchment's runoff routing response. It is found that the CIRFs, upon nondimensionalization, collapsed to common characteristic shapes, which could be explained in terms of the relative contributions of hillslope and channel network flows, and the size of runoff contributing areas. The contributing areas, particularly for the Dunne overland flow, are themselves found to be governed by the competition between drainage of and recharge to the water table, and could be explained by a dimensionless drainage index which quantifies this competition. The study also reveals simple indicators based on landscape properties that can explain the magnitude of travel times in different catchments.
DeePKS: A Comprehensive Data-Driven Approach toward Chemically Accurate Density Functional Theory
We propose a general machine learning-based framework for building an accurate and widely applicable energy functional within the framework of generalized Kohn–Sham density functional theory. To this end, we develop a way of training self-consistent models that are capable of taking large datasets from different systems and different kinds of labels. Here, we demonstrate that the functional that results from this training procedure gives chemically accurate predictions on energy, force, dipole, and electron density for a large class of molecules. It can be continuously improved when more and more data are available.
Phase Equilibrium of Water with Hexagonal and Cubic Ice Using the SCAN Functional
Machine learning models are rapidly becoming widely used to simulate complex physicochemical phenomena with ab initio accuracy. Here, we use one such model as well as direct density functional theory (DFT) calculations to investigate the phase equilibrium of water, hexagonal ice (Ih), and cubic ice (Ic), with an eye toward studying ice nucleation. The machine learning model is based on deep neural networks and has been trained on DFT data obtained using the SCAN exchange and correlation functional. We use this model to drive enhanced sampling simulations aimed at calculating a number of complex properties that are out of reach of DFT-driven simulations and then employ an appropriate reweighting procedure to compute the corresponding properties for the SCAN functional. This approach allows us to calculate the melting temperature of both ice polymorphs, the driving force for nucleation, the heat of fusion, the densities at the melting temperature, the relative stability of ices Ih and Ic, and other properties. We find a correct qualitative prediction of all properties of interest. In some cases, quantitative agreement with experiment is better than for state-of-the-art semiempirical potentials for water. Furthermore, our results also show that SCAN correctly predicts that ice Ih is more stable than ice Ic.
Local hybrid functionals with orbital-free mixing functions and balanced elimination of self-interaction error
The recently introduced density overlap regions indicator (DORI) [P. de Silva and C. Corminboeuf, J. Chem. Theory Comput. 10(9), 3745–3756 (2014)] is a density-dependent scalar field revealing regions of high density overlap between shells, atoms, and molecules. In this work, we exploit its properties to construct local hybrid exchange-correlation functionals aiming at balanced reduction of the self-interaction error. We show that DORI can successfully replace the ratio of the von Weizsäcker and exact positive-definite kinetic energy densities, which is commonly used in mixing functions of local hybrids. Additionally, we introduce several semi-empirical parameters to control the local and global admixture of exact exchange. The most promising of our local hybrids clearly outperforms the underlying semi-local functionals as well as their global hybrids.