Computation, Volume 10, Issue 5 (May 2022) – 14 articles

In this work, nonadiabatic exchange-correlation (XC) potentials for time-dependent density-functional theory (TDDFT) for strongly correlated materials are derived in the limits of strong and weak correlations. After summarizing some essentials of the available dynamical mean-field theory (DMFT) XC potentials valid for these systems, we present details of the Sham–Schluter equation approach that we use to obtain, in principle, an exact XC potential from a many-body theory solution for the nonequilibrium electron self-energy. We derive the XC potentials for the one-band Hubbard model in the limits of weak and strong on-site Coulomb repulsion. To test the accuracy of the obtained potentials, we compare the TDDFT results obtained with these potentials with the corresponding nonequilibrium DMFT solution for the one-band Hubbard model and find that the agreement between the solutions is rather good. We also discuss possible directions to obtain a universal XC potential that would be appropriate for the case of intermediate interaction strengths, i.e., a nonadiabatic potential that can be used to perform TDDFT analysis of nonequilibrium phenomena, such as transport and other ultrafast properties of materials with any strength of electron correlation at any value in the applied perturbing field. Full article

We present a mathematical and numerical framework for the simulation of traffic-induced air pollution in Bamako. We consider a deterministic modeling approach where the spatio-temporal dynamics of the concentrations of air pollutants are governed by a so-called chemical transport model. The time integration and spatial discretization of the model are achieved using the forward Euler algorithm and the finite-element method, respectively. The traffic emissions are estimated using a road traffic simulation package called SUMO. The numerical results for two road traffic-induced air pollutants, namely the carbon monoxide (CO) and the fine particulate matter (PM${}_{2.5}$), support that the proposed framework is suited for reproducing the dynamics of the pollutants specified. Full article

Nowadays, the Oil and Gas (O&G) industry faces significant challenges due to the relentless pressure for rationalization of project expenditure and cost reduction, the demand for greener and renewable energy solutions and the recent outbreak of the pandemic and geopolitical crises. Despite these barriers, the O&G industry still remains a key sector in the growth of world economy, requiring huge capital investments on critical megaprojects. On the other hand, the O&G projects, traditionally, experience cost overruns and delays with damaging consequences to both industry stakeholders and policy-makers. Regarding this, there is an urgent necessity for the adoption of innovative project management methods and tools facilitating the timely delivery of projects with high quality standards complying with budgetary restrictions. Certainly, the success of a project is intrinsically associated with the ability of the decision-makers to estimate, in a compelling way, the monetary resources required throughout the project’s life cycle, an activity that involves various sources of uncertainty. In this study, we focus on the critical management task of evaluating project cost performance through the development of a framework aiming at handling the inherent uncertainty of the estimation process based on well-established data-driven concepts, tools and performance metrics. The proposed framework is demonstrated through a benchmark experiment on a publicly available dataset containing information related to the construction cost of natural gas pipeline projects. The findings derived from the benchmark study showed that the applied algorithm and the adoption of a different feature scaling mechanism presented an interaction effect on the distribution of loss functions, when used as point and interval estimators of the actual cost. Regarding the evaluation of point estimators, Support Vector Regression with different feature scaling mechanisms achieved superior performances in terms of both accuracy and bias, whereas both K-Nearest Neighbors and Classification and Regression Trees variants indicated noteworthy prediction capabilities for producing narrow interval estimates that contain the actual cost value. Finally, the evaluation of the agreement between the performance rankings for the set of candidate models, when used as point and interval estimators revealed a moderate agreement ($a=0.425$). Full article

Machine learning approaches can drastically decrease the computational time for the predictions of spectroscopic properties in materials, while preserving the quality of the computational approaches. We studied the performance of kernel-ridge regression (KRR) and gradient boosting regressor (GBR) models trained on the isotropic shielding values, computed with density-functional theory (DFT), in a series of different known zeolites containing out-of-frame metal cations or fluorine anion and organic structure-directing cations. The smooth overlap of atomic position descriptors were computed from the DFT-optimised Cartesian coordinates of each atoms in the zeolite crystal cells. The use of these descriptors as inputs in both machine learning regression methods led to the prediction of the DFT isotropic shielding values with mean errors within 0.6 ppm. The results showed that the GBR model scales better than the KRR model. Full article

In this study, we theoretically investigate the structural, electronic and magnetic properties of the Cu₂OX₂ (X = Cl, Br, I) compounds. Previous studies reported potential spin-driven ferroelectricity in Cu₂OCl₂, originating from a non-collinear magnetic phase existing below $T_N$∼70 K. However, the nature of this low-temperature magnetic phase is still under debate. Here, we focus on the calculation of J exchange couplings and enhance knowledge in the field by (i) characterizing the low-temperature magnetic order for Cu₂OCl₂ and (ii) evaluating the impact of the chemical pressure on the magnetic interactions, which leads us to consider the two new phases Cu₂OBr₂ and Cu₂OI₂. Our ab initio simulations notably demonstrate the coexistence of strong antiferromagnetic and ferromagnetic interactions, leading to spin frustration. The $T_N$ Néel temperatures were estimated on the basis of a quasi-1D AFM model using the $\mathit{ab}\mathit{initio}$J couplings. It nicely reproduces the $T_N$ value for Cu₂OCl₂ and allows us to predict an increase of $T_N$ under chemical pressure, with $T_N$ = 120 K for the dynamically stable phase Cu₂OBr₂. This investigation suggests that chemical pressure is an effective key factor to open the door of room-temperature multiferroicity. Full article

In this work, generalized polynomial chaos (gPC) expansion for land surface model parameter estimation is evaluated. We perform inverse modeling and compute the posterior distribution of the critical hydrological parameters that are subject to great uncertainty in the Community Land Model (CLM) for a given value of the output LH. The unknown parameters include those that have been identified as the most influential factors on the simulations of surface and subsurface runoff, latent and sensible heat fluxes, and soil moisture in CLM4.0. We set up the inversion problem in the Bayesian framework in two steps: (i) building a surrogate model expressing the input–output mapping, and (ii) performing inverse modeling and computing the posterior distributions of the input parameters using observation data for a given value of the output LH. The development of the surrogate model is carried out with a Bayesian procedure based on the variable selection methods that use gPC expansions. Our approach accounts for bases selection uncertainty and quantifies the importance of the gPC terms, and, hence, all of the input parameters, via the associated posterior probabilities. Full article

Dependence in meta-analytic models can happen due to the same collected data or from the same researchers. The hierarchical Bayesian linear model in a meta-analysis that allows dependence in effect sizes is investigated in this paper. The interested parameters on the hierarchical Bayesian linear dependence (HBLD) model which was developed using the Bayesian techniques will then be estimated. The joint posterior distribution of all parameters for the hierarchical Bayesian linear dependence (HBLD) model is obtained by applying the Gibbs sampling algorithm. Furthermore, in order to measure the robustness of the HBLD model, the sensitivity analysis is conducted using a different prior distribution on the model. This is carried out by applying the Metropolis within the Gibbs algorithm. The simulation study is performed for the estimation of all parameters in the model. The results show that the obtained estimated parameters are close to the true parameters, indicating the consistency of the parameters for the model. The model is also not sensitive because of the changing prior distribution which shows the robustness of the model. A case study, to assess the effects of native-language vocabulary aids on second language reading, is conducted successfully in testing the parameters of the models. Full article

The electronic structure of coordination compounds with lanthanide ions is studied by means of density functional theory (DFT) calculations. This work deals with the electronic structure and properties of open-shell systems based on the calculation of multiplet structure and ligand-field interaction, within the framework of the Ligand–Field Density-Functional Theory (LFDFT) method. Using effective Hamiltonian in conjunction with the DFT, we are able to reasonably calculate the low-lying excited states of the molecular [Eu(NO${}_3$)${}_3$(phenanthroline)${}_2$] complex, subjected to the Eu${}^{3+}$ configuration 4f${}^6$. The results are compared with available experimental data, revealing relative uncertainties of less than 5% for many energy levels. We also demonstrate the ability of the LFDFT method to simulate absorption spectrum, considering cerocene as an example. Ce M${}_{4,5}$ X-ray absorption spectra are simulated for the complexes [Ce(${\eta }^8-$C${}_8$H${}_8$)${}_2$] and [Ce(${\eta }^8-$C${}_8$H${}_8$)${}_2$][Li(tetrahydrofurane)${}_4$], which are approximated by the Ce oxidation states $4+$ and $3+$, respectively. The results showed a very good agreement with the experimental data for the Ce${}^{3+}$ compound, unlike for the Ce${}^{4+}$ one, where charge transfer electronic structure is still missing in the theoretical model. Therefore this presentation reports the benefits of having a theoretical method that is primarily dedicated to coordination chemistry, but it also outlines limitations and places the ongoing developmental efforts in the broader context of treating complex molecular systems. Full article

Despite the progress of therapeutic approaches for treating COVID-19 infection, the interest in developing effective antiviral agents is still high, due to the possibility of the insurgence of viable SARS-CoV-2-resistant strains. Accordingly, in this article, we describe a computational protocol for identifying possible SARS-CoV-2 M^pro covalent inhibitors. Combining several in silico techniques, we evaluated the potential of the peptide-based scaffold with different warheads as a significant alternative to nitriles and aldehyde electrophilic groups. We rationally designed four potential inhibitors containing difluorstatone and a Michael acceptor as warheads. In silico analysis, based on molecular docking, covalent docking, molecular dynamics simulation, and FEP, indicated that the conceived compounds could act as covalent inhibitors of M^pro and that the investigated warheads can be used for designing covalent inhibitors against serine or cysteine proteases such as SARS-CoV-2 M^pro. Our work enriches the knowledge on SARS-CoV-2 M^pro, providing a novel potential strategy for its inhibition, paving the way for the development of effective antivirals. Full article

To better understand graphene and its interactions with polycyclic aromatic hydrocarbons (PAHs), density-functional-theory (DFT) computations were used. Adsorption energy is likely to rise with the number of aromatic rings in the adsorbates. The DFT results revealed that the distance between the PAH molecules adsorbed onto the G ranged between 2.47 and 3.98 Å depending on the structure of PAH molecule. The Non-Covalent Interactions (NCI) plot supports the concept that van der Waals interactions were involved in PAH adsorption onto the Graphene (G) structure. Based on the DFT-calculated adsorption energy data, a rapid and reliable method employing an empirical model of a quantitative structure–activity relationship (QSAR) was created and validated for estimating the adsorption energies of PAH molecules onto graphene. Full article

This paper aims to develop a method for determining margins of static aperiodic stability for electric power systems equipped with distributed generation plants. To this end, we used generalized equations of limiting modes in a stochastic formulation. Computer simulation showed that the developed methodology can be used in solving problems of operational control of the modes of electric power systems. On the basis of the results obtained, we arrived at the following conclusions: the modified equations do not allow the iterative process to converge to a trivial solution and, therefore, they ensure high reliability of results when determining stability margins in a stochastic statement; a technique based on the introduction of an additional variable can be used to improve the convergence of computational processes when determining the stability margins in a deterministic statement; the parameters of the limiting modes obtained in the deterministic and stochastic formulations may significantly differ; with an increase in the variance of the load graphs, the risk of stability violation significantly increases; at the same time, the amount of the margin determined on the basis of the Euclidean norm remains overly optimistic; in the illustrative example, a significant increase in the risk of stability violation takes place during planned and emergency shutdowns of the EPS elements. Full article

The research refers to the modeling of the meaningful and relatively stable visual-figurative and verbal-sign representation of real problems in medical diagnostics of the human organs and systems. The core results of the research are presented. Here, a new visual metalanguage is proposed. It describes the solution of a diagnostic problem by combining several interconnected processes of reasoning in different languages defining “a state of human organs and systems”, “a diagnostic problem” and elements of its decomposition. In the paper, a subject-figurative model of the cognitive hybrid intelligent diagnostic system, its typical architecture, and a synthesis algorithm are provided. Due to the integration of imitation of an internal subject-figurative vision of medical diagnostic problems and the corresponding communication statements of private diagnoses with imitation of the behavior inherent for councils in problem situations, the future implementation of such system prototypes will reduce the number of medical errors. The further stage of this research is the approbation of all solutions for the problem of diagnosing diseases of the pancreas on the materials of the Kaliningrad Regional Clinical Hospital and experimental study of the system. The research is limited by the subject area of medicine but can be generalized to the other areas. Full article

A new approach for basis set generation is reported and tested in helium atom and dimer. The basis sets thus computed, named sigma, range from DZ to 5Z and consist of the same composition as Dunning basis sets but with a different treatment of contractions. The performance of the sigma sets is analyzed for energy and other properties of He atom and He dimer, and the results are compared with those obtained with Dunning and ANO basis sets. The sigma basis sets and their extended versions up to triple augmented provide better energy values than Dunning basis sets of the same composition, and similar values to those attained with the currently available ANO. Extrapolation to complete basis set of correlation energy is compared between the sigma basis sets and those of Dunning, showing the better performance of the former in this respect. Full article