Spin parameter optimization for spin-polarized extended tight-binding methods.

We present an optimization strategy for atom-specific spin-polarization constants within the spin-polarized GFN2-xTB framework, aiming to enhance the accuracy of molecular simulations. We compare a sequential and global optimization of spin parameters for hydrogen, carbon, nitrogen, oxygen, and fluorine. Sensitivity analysis using Sobol indices guides the identification of the most influential parameters for a given reference dataset, allowing for a nuanced understanding of their impact on diverse molecular properties. In the case of the W4-11 dataset, substantial error reduction was achieved, demonstrating the potential of the optimization. Transferability of the optimized spin-polarization constants over different properties, however, is limited, as we demonstrate by applying the optimized parameters on a set of singlet-triplet gaps in carbenes. Further studies on ionization potentials and electron affinities highlight some inherent limitations of current extended tight-binding methods that can not be resolved by simple parameter optimization. We conclude that the significantly improved accuracy strongly encourages the present re-optimization of the spin-polarization constants, whereas the limited transferability motivates a property-specific optimization strategy.

School closures and well-being-related topic searches on Google during the COVID-19 pandemic in Sub-Saharan Africa.

BACKGROUND: Following the outbreak of the 2020 coronavirus, governments adopted non-pharmaceutical interventions (NPIs) to save lives. The NPIs have been deemed to have unintended consequences on mental health and well-being. This study aimed to estimate the impact of the COVID-19 pandemic-induced school closures on the relative search volumes (RSVs) of well-being-relevant topics in 30 low and lower- middle income countries in Sub-Saharan Africa. METHODS: Google Trends search data, difference-in-differences and event study methods were used to evaluate the impact on the related search volume (RSV) of well-being related topic queries in Sub-Saharan Africa. RESULTS: The results suggest positive and significant increases in the search intensity for anger, boredom, fear, sleep, exercise, and prayer. Contrary to other studies, we find no discernible effects on the relative search volumes (RSVs) on loneliness, sadness, and suicide. CONCLUSION: Our findings suggest that the pandemic and the associated restrictions had a mixed effect on well-being-related searches. We recommend increased vigilance and proactive communication from the government and policy makers with the general population in times of emergencies when social policies that restrict lives and liberties need to be adopted.

Multiple linear regression models for predicting the n­octanol/water partition coefficients in the SAMPL7 blind challenge.

A multiple linear regression model called MLR-3 is used for predicting the experimental n-octanol/water partition coefficient (log PN) of 22 N-sulfonamides proposed by the organizers of the SAMPL7 blind challenge. The MLR-3 method was trained with 82 molecules including drug-like sulfonamides and small organic molecules, which resembled the main functional groups present in the challenge dataset. Our model, submitted as "TFE-MLR", presented a root-mean-square error of 0.58 and mean absolute error of 0.41 in log P units, accomplishing the highest accuracy, among empirical methods and also in all submissions based on the ranked ones. Overall, the results support the appropriateness of multiple linear regression approach MLR-3 for computing the n-octanol/water partition coefficient in sulfonamide-bearing compounds. In this context, the outstanding performance of empirical methodologies, where 75% of the ranked submissions achieved root-mean-square errors < 1 log P units, support the suitability of these strategies for obtaining accurate and fast predictions of physicochemical properties as partition coefficients of bioorganic compounds.

Testing automatic methods to predict free binding energy of host-guest complexes in SAMPL7 challenge.

The design of new host-guest complexes represents a fundamental challenge in supramolecular chemistry. At the same time, it opens new opportunities in material sciences or biotechnological applications. A computational tool capable of automatically predicting the binding free energy of any host-guest complex would be a great aid in the design of new host systems, or to identify new guest molecules for a given host. We aim to build such a platform and have used the SAMPL7 challenge to test several methods and design a specific computational pipeline. Predictions will be based on machine learning (when previous knowledge is available) or a physics-based method (otherwise). The formerly delivered predictions with an RMSE of 1.67 kcal/mol but will require further work to identify when a specific system is outside of the scope of the model. The latter is combines the semiempirical GFN2B functional, with docking, molecular mechanics, and molecular dynamics. Correct predictions (RMSE of 1.45 kcal/mol) are contingent on the identification of the correct binding mode, which can be very challenging for host-guest systems with a large number of degrees of freedom. Participation in the blind SAMPL7 challenge provided fundamental direction to the project. More advanced versions of the pipeline will be tested against future SAMPL challenges.

Response to "What Shall We Do with Computed Detonation Performance? Comment on '1,3,4-Oxadiazole Bridges: A Strategy to Improve Energetics at the Molecular Level'".

The values obtained for detonation performance are a function of the computational methods utilized. Since there are many such methods, the literature may contain a range of values for a single compound.

Structural Features of Triethylammonium Acetate through Molecular Dynamics.

I have explored the structural features and the dynamics of triethylammonium acetate by means of semi-empirical (density functional tight binding, DFTB) molecular dynamics. I find that the results from the present simulations agree with recent experimental determinations with only few minor differences in the structural interpretation. A mixture of triethylamine and acetic acid does not form an ionic liquid, but gives rise to a very complex system where ionization is only a partial process affecting only few molecules (1 over 4 experimentally). I have also found that the few ionic couples are stable and remain mainly embedded inside the AcOH neutral moiety.

ACCDB: A collection of chemistry databases for broad computational purposes.

The importance of databases of reliable and accurate data in chemistry has substantially increased in the past two decades. Their main usage is to parametrize electronic structure theory methods, and to assess their capabilities and accuracy for a broad set of chemical problems. The collection we present here-ACCDB-includes data from 16 different research groups, for a total of 44,931 unique reference data points, all at a level of theory significantly higher than density functional theory, and covering most of the periodic table. It is composed of five databases taken from literature (GMTKN, MGCDB84, Minnesota2015, DP284, and W4-17), two newly developed reaction energy databases (W4-17-RE and MN-RE), and a new collection of databases containing transition metals. A set of expandable software tools for its manipulation is also presented here for the first time, as well as a case study where ACCDB is used for benchmarking commercial CPUs for chemistry calculations. © 2018 Wiley Periodicals, Inc.

Structure Optimisation of Large Transition-Metal Complexes with Extended Tight-Binding Methods.

Large transition-metal complexes are used in numerous areas of chemistry. Computer-aided theoretical investigations of such complexes are limited by the sheer size of real systems often consisting of hundreds to thousands of atoms. Accordingly, the development and thorough evaluation of fast semi-empirical quantum chemistry methods that are universally applicable to a large part of the periodic table is indispensable. Herein, we report on the capability of the recently developed GFNn-xTB method family for full quantum-mechanical geometry optimisation of medium to very large transition-metal complexes and organometallic supramolecular structures. The results for a specially compiled benchmark set of 145 diverse closed-shell transition-metal complex structures for all metals up to Hg are presented. Further the GFNn-xTB methods are tested on three established benchmark sets regarding reaction energies and barrier heights of organometallic reactions.

Do Better Quality Embedding Potentials Accelerate the Convergence of QM/MM Models? The Case of Solvated Acid Clusters.

This study examines whether the use of more accurate embedding potentials improves the convergence of quantum mechanics/molecular mechanics (QM/MM) models with respect to the size of the QM region. In conjunction with density functional theory calculations using the ωB97X-D functional, various embedding potentials including the TIP3P water model, the effective fragment potential (EFP), and semi-empirical methods (PM6, PM7, and DFTB) were used to simulate the deprotonation energies of solvated acid clusters. The calculations were performed on solvated neutral (HA) and cationic (HB⁺) acids clusters containing 160 and 480 water molecules using configurations sampled from molecular dynamics simulations. Consistently, the ωB97X-D/EFP model performed the best when using a minimal QM region size. The performance for the other potentials appears to be highly sensitive to the charge character of the acid/base pair. Neutral acids display the expected trend that semi-empirical methods generally perform better than TIP3P; however, an opposite trend was observed for the cationic acids. Additionally, electronic embedding provided an improvement over mechanical embedding for the cationic systems, but not the neutral acids. For the best performing ωB97X-D/EFP model, a QM region containing about 6% of the total number of solvent molecules is needed to approach within 10 kJ mol-1 of the pure QM result if the QM region was chosen based on the distance from the reaction centre.

Shift-and-invert parallel spectral transformation eigensolver: Massively parallel performance for density-functional based tight-binding.

The Shift-and-invert parallel spectral transformations (SIPs), a computational approach to solve sparse eigenvalue problems, is developed for massively parallel architectures with exceptional parallel scalability and robustness. The capabilities of SIPs are demonstrated by diagonalization of density-functional based tight-binding (DFTB) Hamiltonian and overlap matrices for single-wall metallic carbon nanotubes, diamond nanowires, and bulk diamond crystals. The largest (smallest) example studied is a 128,000 (2000) atom nanotube for which ∼330,000 (∼5600) eigenvalues and eigenfunctions are obtained in ∼190 (∼5) seconds when parallelized over 266,144 (16,384) Blue Gene/Q cores. Weak scaling and strong scaling of SIPs are analyzed and the performance of SIPs is compared with other novel methods. Different matrix ordering methods are investigated to reduce the cost of the factorization step, which dominates the time-to-solution at the strong scaling limit. A parallel implementation of assembling the density matrix from the distributed eigenvectors is demonstrated.

Converging ligand-binding free energies obtained with free-energy perturbations at the quantum mechanical level.

In this article, the convergence of quantum mechanical (QM) free-energy simulations based on molecular dynamics simulations at the molecular mechanics (MM) level has been investigated. We have estimated relative free energies for the binding of nine cyclic carboxylate ligands to the octa-acid deep-cavity host, including the host, the ligand, and all water molecules within 4.5 Å of the ligand in the QM calculations (158-224 atoms). We use single-step exponential averaging (ssEA) and the non-Boltzmann Bennett acceptance ratio (NBB) methods to estimate QM/MM free energy with the semi-empirical PM6-DH2X method, both based on interaction energies. We show that ssEA with cumulant expansion gives a better convergence and uses half as many QM calculations as NBB, although the two methods give consistent results. With 720,000 QM calculations per transformation, QM/MM free-energy estimates with a precision of 1 kJ/mol can be obtained for all eight relative energies with ssEA, showing that this approach can be used to calculate converged QM/MM binding free energies for realistic systems and large QM partitions. © 2016 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.

Strengthening Theoretical Testing in Criminology Using Agent-based Modeling.

OBJECTIVES: The Journal of Research in Crime and Delinquency (JRCD) has published important contributions to both criminological theory and associated empirical tests. In this article, we consider some of the challenges associated with traditional approaches to social science research, and discuss a complementary approach that is gaining popularity-agent-based computational modeling-that may offer new opportunities to strengthen theories of crime and develop insights into phenomena of interest. METHOD: Two literature reviews are completed. The aim of the first is to identify those articles published in JRCD that have been the most influential and to classify the theoretical perspectives taken. The second is intended to identify those studies that have used an agent-based model (ABM) to examine criminological theories and to identify which theories have been explored. RESULTS: Ecological theories of crime pattern formation have received the most attention from researchers using ABMs, but many other criminological theories are amenable to testing using such methods. CONCLUSION: Traditional methods of theory development and testing suffer from a number of potential issues that a more systematic use of ABMs-not without its own issues-may help to overcome. ABMs should become another method in the criminologists toolbox to aid theory testing and falsification.

Molecular self-assembled helix peptide nanotubes based on some amino acid molecules and their dipeptides: molecular modeling studies.

CONTEXT: The paper considers the features of the structure and dipole moments of several amino acids and their dipeptides which play an important role in the formation of the peptide nanotubes based on them. The influence of the features of their chirality (left L and right D) and the alpha-helix conformations of amino acids are taken into account. In particular, amino acids with aromatic rings, such as phenylalanine (Phe/F), and branched-chain amino acids (BCAAs)-leucine (Leu/L) and isoleucine (Ile/I)-as well as corresponding dipeptides (diphenylalanine (FF), dileucine (LL), and diisoleucine (II)) are considered. The main features and properties of these dipeptide structures and peptide nanotubes (PNTs), based on them, are investigated using computational molecular modeling and quantum-chemical semi-empirical calculations. Their polar, piezoelectric, and photoelectronic properties and features are studied in detail. The results of calculations of dipole moments and polarization, as well as piezoelectric coefficients and band gap width, for different types of helical peptide nanotubes are presented. The calculated values of the chirality indices of various nanotubes are given, depending on the chirality of the initial dipeptides-the results obtained are consistent with the law of changes in the type of chirality as the hierarchy of molecular structures becomes more complex. The influence of water molecules in the internal cavity of nanotubes on their physical properties is estimated. A comparison of the results of these calculations by various computational methods with the available experimental data is presented and discussed. METHOD: The main tool for molecular modeling of all studied nanostructures in this work was the HyperChem 8.01 software package. The main approach used here is the Hartree-Fock (HF) self-consistent field (SCF) with various quantum-chemical semi-empirical methods (AM1, PM3, RM1) in the restricted Hartree-Fock (RHF) and in the unrestricted Hartree-Fock (UHF) approximations. Optimization of molecular systems and the search for their optimal geometry is carried out in this work using the Polak-Ribeire algorithm (conjugate gradient method), which determines the optimized geometry at the point of their minimum total energy. For such optimized structures, dipole moments D and electronic energy levels (such as EHOMO and ELUMO), as well as the band gap Eg = ELUMO - EHOMO, were then calculated. For each optimized molecular structure, the volume was calculated using the QSAR program implemented also in the HyperChem software package.

Feeling Lonely, Engaging Online During the Outbreak of COVID-19: A Longitudinal Study of Chinese Older Adults.

Background and Objectives: There is growing concern that the coronavirus disease 2019 (COVID-19) pandemic and associated social distancing policies may exacerbate mental health problems in older adults. Most studies primarily have focused on developed countries, whereas the mental consequences of the pandemic in developing countries are less well known. This study evaluates the influences of the global pandemic in relation to loneliness and family contact among older Chinese adults and examines the roles of internet adoption in alleviating the emotional experience of loneliness. Research Design and Methods: We run individual fixed effects regression models using the 2018 and 2020 waves of the Chinese Family Panel Studies survey with 4,944 respondents aged 60 and above to capture the average within-person changes before and after the first outbreak of COVID-19 in China. We further examine the mediating effects of internet adoption on loneliness using structural equation modeling (SEM). Simultaneous SEM tested the effects of internet literacy and social network site use, and heterogeneous effects analyses were also conducted across subgroups. Results: Fixed effects estimates indicated increased loneliness and decreased family contact during the pandemic. Moreover, more older adults were found to use the internet in 2020. The fixed-effect estimations and structural equation models consistently found that older adults' use of the internet contributed to more social contacts and virtual meetings with their children, which have alleviated the emotional experience of loneliness. Discussion and Implications: Digital inclusion of older adults to adopt the internet can be beneficial to improve mental well-being and alleviate loneliness during times of pandemic crisis. The targeted acceleration of digitalization to reduce the digital divide needs to be considered.

A state of art review on estimation of solar radiation with various models.

Solar radiation is free, and very useful input for most sectors such as heat, health, tourism, agriculture, and energy production, and it plays a critical role in the sustainability of biological, and chemical processes in nature. In this framework, the knowledge of solar radiation data or estimating it as accurately as possible is vital to get the maximum benefit from the sun. From this point of view, many sectors have revised their future investments/plans to enhance their profit margins for sustainable development according to the knowledge/estimation of solar radiation. This case has noteworthy attracted the attention of researchers for the estimation of solar radiation with low errors. Accordingly, it is noticed that various types of models have been continuously developed in the literature. The present review paper has mainly centered on the solar radiation works estimated by the empirical models, time series, artificial intelligence algorithms, and hybrid models. In general, these models have needed the atmospheric, geographic, climatic, and historical solar radiation data of a given region for the estimation of solar radiation. It is seen from the literature review that each model has its advantages and disadvantages in the estimation of solar radiation, and a model that gives the best results for one region may give the worst results for the other region. Furthermore, it is noticed that an input parameter that strongly improves the performance success of the models for a region may worsen the performance success of another region. In this direction, the estimation of solar radiation has been separately detailed in terms of empirical models, time series, artificial intelligence algorithms, and hybrid algorithms. Accordingly, the research gaps, challenges, and future directions for the estimation of solar radiation have been drawn in the present study. In the results, it is well-observed that the hybrid models have exhibited more accurate and reliable results in most studies due to their ability to merge between different models for the benefit of the advantages of each model, but the empirical models have come to the fore in terms of ease of use, and low computational costs.

Molecular modelling and computational studies of peptide diphenylalanine nanotubes, containing waters: structural and interactions analysis.

The work is devoted to computer studies of the structural and physical properties of such self-organizing structures as peptide nanotubes (PNT) based on diphenylalanine (FF) dipeptide with different initial isomers of the left (L-FF) and right (D-FF) chiralities of these dipeptides. The structures under study are considered both with empty anhydrous and with internal cavities filled with water molecules. Molecular models of both chiralities are investigated using quantum-chemical DFT and semi-empirical methods, which are in consistent with the known experimental data. To study the effect of nano-sized clusters of water molecules embedded in the inner hydrophilic cavity on the properties of nanotubes (including the changes in their dipole moments and polarizations), as well as the changes in the structure and properties of water clusters themselves (their own dipole moments and polarizations), the surfaces of internal cavities of nanotubes and outer surfaces of water cluster structures for both types of chirality are analyzed. A specially developed method of visual differential analysis of structural features of (bio)macromolecular structures is applied for these studies. The results obtained of a number of physical properties (interacting energies, dipole moments, polarization values) are given for various cases and analyzed in comparison with the known data. These data are necessary for analyzing the interactions of water molecules with hydrophilic parts of nanotube molecules based on FF, such as COO- and NH3 + , since they determine many properties of the structures under study. The data obtained are useful for further analysis of the possible adhesion and capture of medical molecular components by active layers of FF-based PNT, which can be designed for creating capsules for targeted delivery of pharmaceuticals and drugs on their basis.

The evolution of epistemological methodologies in anatomy: From antiquity to modern times.

Present day scenario regarding epistemological methods in anatomy is in sharp contrast to the situation during ancient period. This study aimed to explore the evolution of epistemological methodologies in anatomy across centuries. In ancient times Egyptian embalmers acquired anatomical knowledge from handling human bodies and likewise anatomical studies in India involved human dissection. Ancient Greeks used theological principles-based methods, animal dissection and human dissection in practice of anatomy. Human dissection was also practiced in ancient China for gaining anatomical knowledge. Prohibition of human dissection led to use of animal dissection in ancient Rome and the trend continued in Europe through Middle Ages. Epistemological methods used by Muslim scholars during Middle Ages are not clearly chronicled. Human dissection returned as primary epistemological method in Renaissance Europe and empirical methods were reinstated after ancient period in human dissection during 16th century. The situation further improved with introduction of pragmatic experiment based approach during 17th century and autopsy-based methods during 18th century. Advances in anatomical knowledge continued with advent of microscope-based methods and emergence of anatomical sections in practice of human dissection in 19th century. Introduction of human observational studies, medical imaging, and molecular methods presented more options in terms of epistemological methods for investigating the human body during 20th century. Onset of 21st century has witnessed dominance of technology-based methods in anatomy. Limited emphasis on ethics in epistemological methodologies since antiquity is a dark aspect of otherwise an eventful evolutionary journey but recent developments are in positive direction.

Binding free energy calculation with QM/MM hybrid methods for Abl-Kinase inhibitor.

We report a Quantum mechanics/Molecular Mechanics-Poisson-Boltzmann/ Surface Area (QM/MM-PB/SA) method to calculate the binding free energy of c-Abl human tyrosine kinase by combining the QM and MM principles where the ligand is treated quantum mechanically and the rest of the receptor by classical molecular mechanics. To study the role of entropy and the flexibility of the protein ligand complex in a solvated environment, molecular dynamics calculations are performed using a hybrid QM/MM approach. This work shows that the results of the QM/MM approach are strongly correlated with the binding affinity. The QM/MM interaction energy in our reported study confirms the importance of electronic and polarization contributions, which are often neglected in classical MM-PB/SA calculations. Moreover, a comparison of semi-empirical methods like DFTB-SCC, PM3, MNDO, MNDO-PDDG, and PDDG-PM3 is also performed. The results of the study show that the implementation of a DFTB-SCC semi-empirical Hamiltonian that is derived from DFT gives better results than other methods. We have performed such studies using the AMBER molecular dynamic package for the first time. The calculated binding free energy is also in agreement with the experimentally determined binding affinity for c-Abl tyrosine kinase complex with Imatinib.Electronic supplementary material The online version of this article (doi:10.1007/s10867-010-9199-z) contains supplementary material, which is available to authorized users.

Optimized SQE atomic charges for peptides accessible via a web application.

BACKGROUND: Partial atomic charges find many applications in computational chemistry, chemoinformatics, bioinformatics, and nanoscience. Currently, frequently used methods for charge calculation are the Electronegativity Equalization Method (EEM), Charge Equilibration method (QEq), and Extended QEq (EQeq). They all are fast, even for large molecules, but require empirical parameters. However, even these advanced methods have limitations-e.g., their application for peptides, proteins, and other macromolecules is problematic. An empirical charge calculation method that is promising for peptides and other macromolecular systems is the Split-charge Equilibration method (SQE) and its extension SQE+q0. Unfortunately, only one parameter set is available for these methods, and their implementation is not easily accessible. RESULTS: In this article, we present for the first time an optimized guided minimization method (optGM) for the fast parameterization of empirical charge calculation methods and compare it with the currently available guided minimization (GDMIN) method. Then, we introduce a further extension to SQE, SQE+qp, adapted for peptide datasets, and compare it with the common approaches EEM, QEq EQeq, SQE, and SQE+q0. Finally, we integrate SQE and SQE+qp into the web application Atomic Charge Calculator II (ACC II), including several parameter sets. CONCLUSION: The main contribution of the article is that it makes SQE methods with their parameters accessible to the users via the ACC II web application ( https://acc2.ncbr.muni.cz ) and also via a command-line application. Furthermore, our improvement, SQE+qp, provides an excellent solution for peptide datasets. Additionally, optGM provides comparable parameters to GDMIN in a markedly shorter time. Therefore, optGM allows us to perform parameterizations for charge calculation methods with more parameters (e.g., SQE and its extensions) using large datasets.

A critical and intensive review on assessment of water quality parameters through geospatial techniques.

Evaluation of water quality is a priority work nowadays. In order to monitor and map, the water quality for a wide range on different scales (spatial, temporal), the geospatial technique has the potential to minimize the field and laboratory work. The review has emphasized the advance of remote sensing for the effectiveness of spectral analysis, bio-optical estimation, empirical method, and application of machine learning for water quality assessment. The water quality parameters (turbidity, suspended particles, chlorophyll, etc.) and their retrieval techniques are described in a scientific manner. Available satellite, bands, resolution, and spectrum ranges for specific parameters are critically described in this review with challenges in remote sensing for water quality analysis, considering non-optical active parameters. The application of statistical programmes like linear (multiple regression analysis) and non-linear approaches is discussed for better assessment of water quality. Emphasis is given on comparison between different models to increase the accuracy level of remote sensing of water quality assessment. A direction is suggested for future development in the field of estimation of water pollution assessment through geospatial techniques.