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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.
Surface Composition, Work Function, and Electrochemical Characteristics of Gallium-Doped Zinc Oxide
Gallium-doped zinc oxide (GZO) possesses the electric conductivity, thermal stability, and earth abundance to be a promising transparent conductive oxide replacement for indium tin oxide electrodes in a number of molecular electronic devices, including organic solar cells and organic light emitting diodes. The surface chemistry of GZO is complex and dominated by the hydrolysis chemistry of ZnO, which influences the work function via charge transfer and band bending caused by adsorbates. A comprehensive characterization of the surface chemical composition and electrochemical properties of GZO electrodes is presented, using both solution and surface adsorbed redox probe molecules. The GZO surface is characterized using monochromatic X-ray photoelectron spectroscopy and ultraviolet photoelectron spectroscopy after the following pretreatments: (i) hydriodic acid etch, (ii) potassium hydroxide etch, (iii) RF oxygen plasma etching, and (iv) high-vacuum argon-ion sputtering. The O 1s spectra for the GZO electrodes have contributions from the stoichiometric oxide lattice, defects within the lattice, hydroxylated species, and carbonaceous impurities, with relative near-surface compositions varying with pretreatment. Solution etching procedures result in an increase of the work function and ionization potential of the GZO electrode, but yield different near surface Zn:Ga atomic ratios, which significantly influence charge transfer rates for a chemisorbed probe molecule. The near surface chemical composition is shown to be the dominant factor in controlling surface work function and significantly influences the rate of electron transfer to both solution and tethered probe molecules.
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.
Structure and functional interaction of the extracellular domain of human GABA[subscript B] receptor GBR2
Inhibitory neurotransmission is mediated primarily by GABA. The metabotropic GABA{sub B} receptor is a G protein-coupled receptor central to mammalian brain function. Malfunction of GABA{sub B} receptor has been implicated in several neurological disorders. GABA{sub B} receptor functions as a heterodimeric assembly of GBR1 and GBR2 subunits, where GBR1 is responsible for ligand-binding and GBR2 is responsible for G protein coupling. Here we demonstrate that the GBR2 ectodomain directly interacts with the GBR1 ectodomain to increase agonist affinity by selectively stabilizing the agonist-bound conformation of GBR1. We present the crystal structure of the GBR2 ectodomain, which reveals a polar heterodimeric interface. We also identify specific heterodimer contacts from both subunits, and GBR1 residues involved in ligand recognition. Lastly, our structural and functional data indicate that the GBR2 ectodomain adopts a constitutively open conformation, suggesting a structural asymmetry in the active state of GABA{sub B} receptor that is unique to the GABAergic system.
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.
Implementation of density functional embedding theory within the projector-augmented-wave method and applications to semiconductor defect states
We report a new implementation of the density functional embedding theory (DFET) in the VASP code, using the projector-augmented-wave (PAW) formalism. Newly developed algorithms allow us to efficiently perform optimized effective potential optimizations within PAW. The new algorithm generates robust and physically correct embedding potentials, as we verified using several test systems including a covalently bound molecule, a metal surface, and bulk semiconductors. We show that with the resulting embedding potential, embedded cluster models can reproduce the electronic structure of point defects in bulk semiconductors, thereby demonstrating the validity of DFET in semiconductors for the first time. Compared to our previous version, the new implementation of DFET within VASP affords use of all features of VASP (e.g., a systematic PAW library, a wide selection of functionals, a more flexible choice of U correction formalisms, and faster computational speed) with DFET. Furthermore, our results are fairly robust with respect to both plane-wave and Gaussian type orbital basis sets in the embedded cluster calculations. This suggests that the density functional embedding method is potentially an accurate and efficient way to study properties of isolated defects in semiconductors.
Perspective: Fundamental aspects of time-dependent density functional theory
In the thirty-two years since the birth of the foundational theorems, time-dependent density functional theory has had a tremendous impact on calculations of electronic spectra and dynamics in chemistry, biology, solid-state physics, and materials science. Alongside the wide-ranging applications, there has been much progress in understanding fundamental aspects of the functionals and the theory itself. This Perspective looks back to some of these developments, reports on some recent progress and current challenges for functionals, and speculates on future directions to improve the accuracy of approximations used in this relatively young theory.
Community structure – Ecosystem function relationships in the Congo Basin methane cycle depend on the physiological scale of function
Belowground ecosystem processes can be highly variable and difficult to predict using microbial community data. Here, we argue that this stems from at least three issues: (a) complex covariance structure of samples (with environmental conditions or spatial proximity) can make distinguishing biotic drivers a challenge; (b) communities can control ecosystem processes through multiple mechanisms, making the identification of these controls a challenge; and (c) ecosystem function assessments can be broad in physiological scale, encapsulating multiple processes with unique microbially mediated controls. In this work, we test these assertions using methane (CH 4 )-cycling processes in soil samples collected along a wetland-to-upland habitat gradient in the Congo Basin. We perform our measurements of function under controlled laboratory conditions and statistically control for environmental covariates to aid in identifying biotic drivers. We divide measurements of microbial communities into four attributes (abundance, activity, composition, and diversity) that represent different forms of community control. Lastly, our process measurements differ in physiological scale, including broader processes (gross methanogenesis and methanotrophy) that involve more mediating groups, to finer processes (hydrogenotrophic methanogenesis and high-affinity CH 4 oxidation) with fewer mediating groups. We observed that finer scale processes can be more readily predicted from microbial community structure than broader scale processes. In addition, the nature of those relationships differed, with broad processes limited by abundance while fine-scale processes were associated with diversity and composition. These findings demonstrate the importance of carefully defining the physiological scale of ecosystem function and performing community measurements that represent the range of possible controls on ecosystem processes.
Asymptotic Normalization Coefficients (Nuclear Vertex Constants), Three-Body Asymptotic Normalization Functions (On-Shell Vertex Functions) and Nuclear Astrophysics
In the present review work, the main methods of the determination of asymptotic normalization coefficients (or respective nuclear vertex constants) for the A + x → B channel and of threebody asymptotic normalization functions (or respective on-shell vertex functions) for the A + b + c → B channel are briefly discussed. The main attention is paid both to the use of the specific asymptotic normalization coefficients and three-body asymptotic normalization functions as a source of getting the valuable information about the pair (nucleon–nucleon, nucleon–cluster and cluster–cluster) nuclear interactions and to their application for the specific direct nuclear-astrophysical radiative capture and peripheral transfer reactions at low energies.
Progress at the interface of wave-function and density-functional theories
The Kohn-Sham (KS) potential of density-functional theory (DFT) emerges as the minimizing effective potential in a variational scheme that does not involve fixing the unknown single-electron density. Using Rayleigh Schroedinger (RS) perturbation theory (PT), we construct ab initio approximations for the energy difference, the minimization of which determines the KS potential directly - thereby bypassing DFT's traditional algorithm to search for the density that minimizes the total energy. From second-order RS PT, we obtain variationally stable energy differences to be minimized, solving the severe problem of variational collapse of orbital-dependent exchange-correlation functionals based on second-order RS PT.