Does Aromaticity Play a Role in Electronic and Structural Properties of YBn (n=2-14) Clusters?

Nanoclusters exhibit electronic, optical, and magnetic properties that differ significantly from those of extended and molecular systems with comparable stoichiometries. In this work, we examined the structural, energetic, and electronic characteristics of yttrium-doped boron clusters (YBn,  n =2-14) with robust wavefunction analysis tools. Special emphasis is placed on the elucidation of the potential aromatic character exhibited by the resultant molecules and how it can affect their chemical bonding and stability. Our results revealed that the YBn stability is governed by the maximization of the ionic Y-B interactions. This is evidenced from the lowest-energy conformations, which manifest as half-sandwich structures wherein the majority of boron atoms are bonded to yttrium. The stabilization of such chemical contacts comes at the expense of a notorious depletion of the Y local electron density, crystallizing in a considerable ionic character, close to Y2+ + Bn2-. Such a  charge transfer is coupled to the enhancement of the electron delocalization within the YBn lattice, resulting in quite remarkable local and global aromatic characters. Altogether, this study shows how the toolkit of real space chemical bonding descriptors can offer valuable insights into the structural and electronic properties,  of YBn clusters, contributing to a better understanding of their behavior.

An Unsupervised Machine Learning Approach for the Automatic Construction of Local Chemical Descriptors.

Condensing the many physical variables defining a chemical system into a fixed-size array poses a significant challenge in the development of chemical Machine Learning (ML). Atom Centered Symmetry Functions (ACSFs) offer an intuitive featurization approach by means of a tedious and labor-intensive selection of tunable parameters. In this work, we implement an unsupervised ML strategy relying on a Gaussian Mixture Model (GMM) to automatically optimize the ACSF parameters. GMMs effortlessly decompose the vastness of the chemical and conformational spaces into well-defined radial and angular clusters, which are then used to build tailor-made ACSFs. The unsupervised exploration of the space has demonstrated general applicability across a diverse range of systems, spanning from various unimolecular landscapes to heterogeneous databases. The impact of the sampling technique and temperature on space exploration is also addressed, highlighting the particularly advantageous role of high-temperature Molecular Dynamics (MD) simulations. The reliability of the resulting features is assessed through the estimation of the atomic charges of a prototypical capped amino acid and a heterogeneous collection of CHON molecules. The automatically constructed ACSFs serve as high-quality descriptors, consistently yielding typical prediction errors below 0.010 electrons bound for the reported atomic charges. Altering the spatial distribution of the functions with respect to the cluster highlights the critical role of symmetry rupture in achieving significantly improved features. More specifically, using two separate functions to describe the lower and upper tails of the cluster results in the best performing models with errors as low as 0.006 electrons. Finally, the effectiveness of finely tuned features was checked across different architectures, unveiling the superior performance of Gaussian Process (GP) models over Feed Forward Neural Networks (FFNNs), particularly in low-data regimes, with nearly a 2-fold increase in prediction quality. Altogether, this approach paves the way toward an easier construction of local chemical descriptors, while providing valuable insights into how radial and angular spaces should be mapped. Finally, this work opens the possibility of encoding many-body information beyond angular terms into upcoming ML features.

Radical revelations: the pnictogen effect in linear acetylenes.

Acetylenes are essential building blocks in modern chemistry due to their remarkable modularity. The introduction of heteroatoms, such as pnictogens (X), is one of the simplest approaches to altering the C≡C bond. However, the chemistry of the resultant dipnictogenoacetylenes (DXAs) is strongly dependent on the nature of X. In this work, rigorous theoretical chemistry tools are employed to shed light on the origin of these differences, providing a detailed evaluation of the impact of X on the geometrical and electronic features of DXAs. Special emphasis is made on the study of the carbene character of the systems through the analysis of the interconversion mechanism between the linear and zigzag isomers. Our results show that second-period atoms behave drastically differently to the remaining X: down the group, a zwitterionic resonance form emerges at the expense of decreasing the carbenoid role, eventually resulting in an electrostatically driven ring closure. Furthermore, our findings pave the way to potentially unveiling novel routes for the promotion of free-radical chemistry.

Developing a User-Friendly Code for the Fast Estimation of Well-Behaved Real-Space Partial Charges.

The Quantum Theory of Atoms in Molecules (QTAIM) provides an intuitive, yet physically sound, strategy to determine the partial charges of any chemical system relying on the topology induced by the electron density ρ(r) . In a previous work [J. Chem. Phys. 2022, 156, 014112], we introduced a machine learning (ML) model for the computation of QTAIM charges of C, H, O, and N atoms at a fraction of the conventional computational cost. Unfortunately, the independent nature of the atomistic predictions implies that the raw atomic charges may not necessarily reconstruct the exact molecular charge, limiting the applicability of the latter in the chemistry realm. Trying to solve such an inconvenience, we introduce NNAIMGUI, a user-friendly code which combines the inferring abilities of ML with an equilibration strategy to afford adequately behaved partial charges. The performance of this approach is put to the test in a variety of scenarios including interpolation and extrapolation regimes (e.g chemical reactions) as well as large systems. The results of this work prove that the equilibrated charges retain the chemically accurate behavior reproduced by the ML models. Furthermore, NNAIMGUI is a fully flexible architecture allowing users to train and use tailor-made models targeted at any atomic property of choice. In this way, the GUI-interfaced code, equipped with visualization utilities, makes the computation of real-space atomic properties much more appealing and intuitive, paving the way toward the extension of QTAIM related descriptors beyond the theoretical chemistry community.

Wave function analyses of scandium-doped aluminium clusters, AlnSc (n = 1-24), and their CO2 fixation abilities.

Nanoclusters represent a connection between (i) solid state systems and (ii) species in the atomic and molecular domains. Additionally, nanoclusters can also have very interesting electronic, optical and magnetic properties. For example, some aluminium clusters behave as superatoms and the doping of these clusters might strengthen their adsorption capabilities. Thus, we address herein the structural, energetic and electronic characterisation of scandium-doped aluminium clusters (AlnSc (n = 1-24)) by means of density functional theory calculations and quantum chemical topology wave function analyses. We studied the effect of Sc-doping on the structure and charge distribution by considering pure Al clusters as well. The quantum theory of atoms in molecules (QTAIM) reveals that interior Al atoms have large negative atomic charges (≈2a.u.) and hence the atoms surrounding them are considerably electron deficient. The Interacting Quantum Atoms (IQA) energy partition allowed us to establish the nature of the interaction between the Al13 superatom and the Al12Sc cluster with Al to form the complexes Al14 and Al13Sc, respectively. We also used the IQA approach to examine (i) the influence of Sc on the geometry of the AlnSc complexes along with (ii) the cooperative effects in the binding of AlnSc and Aln+1 clusters. We also exploited the QTAIM and IQA approaches to study the interaction of the electrophilic surface of the examined systems with CO2. Overall, we observe that the investigated Sc-doped Al complexes with a marked stability towards disproportionation reactions exhibit strong adsorption energies with CO2. Concomitantly, the carbon dioxide molecule is considerably distorted and destabilised, conditions which might prepare it for further chemical reactions. Altogether, this paper gives valuable insights on the tuning of the properties of metallic clusters for their design and exploitation in custom-made materials.

Calculation of the ELF in the excited state with single-determinant methods.

Since its first definition, back in 1990, the electron localization function (ELF) has settled as one of the most commonly employed techniques to characterize the nature of the chemical bond in real space. Although most of the work using the ELF has focused on the study of ground-state chemical reactivity, a growing interest has blossomed to apply these techniques to the nearly unexplored realm of excited states and photochemistry. Since accurate excited electronic states usually require to account appropriately for electron correlation, the standard single-determinant ELF formulation cannot be blindly applied to them, and it is necessary to turn to correlated ELF descriptions based on the two-particle density matrix (2-PDM). The latter requires costly wavefunction approaches, unaffordable for most of the systems of current photochemical interest. Here, we compare the exact, 2-PDM-based ELF results with those of approximate 2-PDM reconstructions taken from reduced density matrix functional theory. Our approach is put to the test in a wide variety of representative scenarios, such as those provided by the lowest-lying excited electronic states of simple diatomic and polyatomic molecules. Altogether, our results suggest that even approximate 2-PDMs are able to accurately reproduce, on a general basis, the topological and statistical features of the ELF scalar field, paving the way toward the application of cost-effective methodologies, such as time-dependent-Hartree-Fock or time-dependent density functional theory, in the accurate description of the chemical bonding in excited states of photochemical relevance.

COVID-19 Bereavement in Ten Latin American Countries: Measurement Invariance of the Pandemic Grief Scale and Its Relation to Suicidal Ideation.

The present study aimed to evaluate the cross-cultural measurement invariance of the Pandemic Grief Scale (PGS) in ten Latin American countries. A total of 2,321 people who had lost a family member or other loved one due to COVID-19 participated, with a mean age of 34.22 years old (SD = 11.99). In addition to the PGS, a single item of suicidal ideation was applied. The unidimensional model of the PGS had adequate fit in most countries and good reliability estimates. There was evidence of measurement invariance by country and gender. Also, a one-point increase in the PGS was associated with an almost twofold increase in the odds of suicidal ideation. Scores greater than or equal to 4 on the PGS are proposed as a cut off to identify individuals with suicidal ideation. Strong evidence of the cross-cultural validity of the PGS is provided.

Assessment of Obsessive Thoughts About COVID-19 in 7 Latin American Countries: Structure and Measurement Invariance of the Obsession With COVID-19 Scale.

The present study aimed to evaluate the measurement invariance of the Obsession with COVID-19 Scale (OCS) among seven Latin American countries: Bolivia, Brazil, Cuba, El Salvador, Guatemala, Paraguay, and Uruguay. Although the OCS has been used in several countries and languages, there is a need for approaches that better integrate the cross-cultural equivalence of the scale. A total of 3185 people participated in the study. The results indicated the presence of a unidimensional structure and good reliability indices for the OCS in each country. The alignment method indicated that the OCS is an invariant measure of COVID-19 obsession among the populations of seven Latin American countries. The findings based on IRT analysis indicated that all OCS items had adequate discrimination and difficulty parameters. The findings contribute to the understanding of the internal structure of the scale in different countries at the same time, something that has been pending evaluation.

A real space picture of the role of steric effects in SN 2 reactions.

Within substitution reactions, the Bimolecular Nucleophilic Substitution (SN 2) reaction mechanism is one of the most frequently found and studied ones. Among other factors, the easiness of the SN 2 pathway is classically considered to be determined by steric hindrance. However, the diffuse nature of the latter inevitably darkens these and other arguments holding the pillars of chemical intuition. In this work, we employ the steric energy (EST ) descriptor, formulated within the Interacting Quantum Atoms approach, to offer insights regarding this problem. The steric demands of the substrate, nucleophile and leaving group were studied using the gas-phase SN 2 reaction with different organic skeletons (CH3 , CH3 CH2 , (CH3 )2 CH, (CH3 )3 C, (CH3 )3 CCH2 ) and halogens (F, Cl, and Br) as test-bed systems. Our results show that, according to EST , the SH experienced along these simple reactions fits, in the general case, the trends predicted by a meticulous and rigorous application of chemical intuition. However, steric clash alone should not be considered as the only argument used to explain the easiness of the SN 2 reaction over different electrophiles.

New venues in electron density analysis.

We provide a comprehensive overview of the chemical information from electron density: not only how to extract information, but also how to obtain and how to assess the quality of the electron density itself. After introducing several indexes derived from electron density, which allow bonding to be revealed, we focus on the various potential sources of electron density, and also explain the error trends they show so that a judicious choice of methods and limitations are clearly laid on the table. Computational, experimental-computational combinations, and machine learning efforts are covered in this work.

Does Steric Hindrance Actually Govern the Competition between Bimolecular Substitution and Elimination Reactions?

Bimolecular nucleophilic substitution (SN2) and elimination (E2) reactions are prototypical examples of competing reaction mechanisms, with fundamental implications in modern chemical synthesis. Steric hindrance (SH) is often considered to be one of the dominant factors determining the most favorable reaction out of the SN2 and E2 pathways. However, the picture provided by classical chemical intuition is inevitably grounded on poorly defined bases. In this work, we try to shed light on the aforementioned problem through the analysis and comparison of the evolution of the steric energy (EST), settled within the IQA scheme and experienced along both reaction mechanisms. For such a purpose, the substitution and elimination reactions of a collection of alkyl bromides (R-Br) with the hydroxide anion (OH-) were studied in the gas phase at the M06-2X/aug-cc-pVDZ level of theory. The results show that, generally, EST recovers the appealing trends already anticipated by chemical intuition and organic chemistry, supporting the role that SH is classically claimed to play in the competition between SN2 and E2 reactions.

NNAIMQ: A neural network model for predicting QTAIM charges.

Atomic charges provide crucial information about the electronic structure of a molecular system. Among the different definitions of these descriptors, the one proposed by the Quantum Theory of Atoms in Molecules (QTAIM) is particularly attractive given its invariance against orbital transformations although the computational cost associated with their calculation limits its applicability. Given that Machine Learning (ML) techniques have been shown to accelerate orders of magnitude the computation of a number of quantum mechanical observables, in this work, we take advantage of ML knowledge to develop an intuitive and fast neural network model (NNAIMQ) for the computation of QTAIM charges for C, H, O, and N atoms with high accuracy. Our model has been trained and tested using data from quantum chemical calculations in more than 45 000 molecular environments of the near-equilibrium CHON chemical space. The reliability and performance of NNAIMQ have been analyzed in a variety of scenarios, from equilibrium geometries to molecular dynamics simulations. Altogether, NNAIMQ yields remarkably small prediction errors, well below the 0.03 electron limit in the general case, while accelerating the calculation of QTAIM charges by several orders of magnitude.

Fear of COVID-19 scale: Validity, reliability and factorial invariance in Argentina's general population.

This study evaluated the psychometric properties of the Scale of Fear of COVID-19 (FCV-19S) in a sample of 1,291 Argentines. The two-related factor structure of the FCV-19S had satisfactory goodness-of-fit indices using structural equation modeling and item response theory. Further results showed that the reliability was adequate, the factor structure was strictly invariable across age groups, and the model that evaluated the relationships between fear of COVID-19, anxiety, and depression had adequate goodness of fit indices as well. The results indicated that FCV-19S has strong psychometric properties to measure fear of COVID-19 in the general population of Argentina.

Is it possible to find a bifactor structure in the Fear of COVID-19 Scale (FCV-19S)? Psychometric evidence in an Ecuadorian sample.

The aim of the study was to evaluate the construct validity based on the internal structure, the relationship with other variables, and the internal consistency among items of the Fear of COVID-19 Scale (FCV-19S) in a sample of 743 Ecuadorians. The findings confirm the presence of a bifactor structure, which includes a general factor and two specific factors: one emotional and the other physiological. The general factor, and the specific factors presented adequate levels of internal consistency. Finally, the FCV-19S showed a highly significant relationship with GAD-7 at the latent level. The scale has adequate psychometric properties for its application.

Obsession with Covid-19 in Peruvian police and armed forces: Validation of the obsession with Covid-19 Scale in Spanish using SEM and IRT models.

The study evaluated the psychometric properties of the Spanish version of the Obsession with COVID-19 Scale (OCS) in 214 police and members of the armed forces (Mage = 29.33 years, SD = 11.28). The one-dimensionality and satisfactory reliability of OCS were confirmed with confirmatory factor analysis, Item Response Theory analysis, Cronbach's alpha, and McDonald's omega. The scale is useful for identifying individuals with low levels of persistent and disturbing thoughts about COVID-19. COVID-19 obsession was associated with COVID-19 fear, anxiety, and depression. The OCS is suitable for investigating the psychological impact of COVID-19 on members of the police and armed forces.

Cross-cultural measurement invariance of the fear of COVID-19 scale in seven Latin American countries.

The objective was to evaluate the cross-cultural measurement invariance of the Fear of COVID-19 Scale (FCV-19S) in 7 Latin American countries (Colombia, Ecuador, El Salvador, Mexico, Paraguay, Argentina, and Uruguay). The participants were 2944 people, selected through non-probability sampling for convenience, where the majority were women. Data collection occurred between 12 June and 14 September 2020. The results indicated that the model with two related factors presents a better fit to the data and has partial scalar invariance among the 7 countries. Differences in emotional and physiological reactions were observed between the countries.

A QCT View of the Interplay between Hydrogen Bonds and Aromaticity in Small CHON Derivatives.

The somewhat elusive concept of aromaticity plays an undeniable role in the chemical narrative, often being considered the principal cause of the unusual properties and stability exhibited by certain π skeletons. More recently, the concept of aromaticity has also been utilised to explain the modulation of the strength of non-covalent interactions (NCIs), such as hydrogen bonding (HB), paving the way towards the in silico prediction and design of tailor-made interacting systems. In this work, we try to shed light on this area by exploiting real space techniques, such as the Quantum Theory of Atoms in Molecules (QTAIM), the Interacting Quantum Atoms (IQA) approaches along with the electron delocalisation indicators Aromatic Fluctuation (FLU) and Multicenter (MCI) indices. The QTAIM and IQA methods have been proven capable of providing an unbiased and rigorous picture of NCIs in a wide variety of scenarios, whereas the FLU and MCI descriptors have been successfully exploited in the study of diverse aromatic and antiaromatic systems. We used a collection of simple archetypal examples of aromatic, non-aromatic and antiaromatic moieties within organic molecules to examine the changes in π delocalisation and aromaticity induced by the Aromaticity and Antiaromaticity Modulated Hydrogen Bonds (AMHB). We observed fundamental differences in the behaviour of systems containing the HB acceptor within and outside the ring, e.g., a destabilisation of the rings in the former as opposed to a stabilisation of the latter upon the formation of the corresponding molecular clusters. The results of this work provide a physically sound basis to rationalise the strengthening and weakening of AMHBs with respect to suitable non-cyclic non-aromatic references. We also found significant differences in the chemical bonding scenarios of aromatic and antiaromatic systems in the formation of AMHB. Altogether, our investigation provide novel, valuable insights about the complex mutual influence between hydrogen bonds and π systems.

What is post-COVID-19 syndrome? Definition and update.

This work addresses the origin and development of post-COVID-19 syndrome, which consists of the persistence of different symptoms over time as a consequence of SARS-CoV-2 infection. Based on a narrative review of the scientific literature, a brief analysis of the new term is made, specifying the conceptual definition, characteristic symptoms, the various implications for people's health, and the responses to specific care measures that have been implemented. It concludes with a wake-up call to the governments of Latin America and the Caribbean in order for care and surveillance to be provided to this public health problem.

Este trabajo aborda el origen y el desarrollo del síndrome pos-COVID-19, el cual consiste en la persistencia a través del tiempo de diferentes síntomas como consecuencia de la infección por SARS-CoV-2. A partir de una revisión narrativa de la literatura científica, se realiza un breve recorrido sobre el nuevo término y se precisa la definición conceptual, los síntomas característicos, las diversas implicaciones en la salud de las personas y las respuestas de atención específica que se han implementado. Se concluye con una llamada de atención a los gobiernos de América Latina y el Caribe para dar tratamiento y seguimiento a este problema de salud pública.

Cross-cultural measurement invariance of the purpose in life test - Short form (PIL-SF) in seven Latin American countries.

The aim was to test the cross-cultural measurement invariance of the PIL-SF in a sample of people from seven Latin American countries. Additionally, the characteristics of the PIL-SF items were evaluated and to assess the relationship between purpose in life, as measured by the PIL-SF, and fear of COVID-19. A total of 4306 people from seven Latin American countries participated in the study. The results indicated that the PIL-SF is invariant in the seven participating countries and, therefore, there is evidence that the items reflect the purpose of life in the same way in all countries. This allows comparisons of purpose in life between countries that are free of bias, reflecting the true differences in how countries respond to items. From IRT, the discrimination parameters are adequate and indicate that the items cover a wide range of the purpose in life construct. The difficulty parameters are adequate and increase monotonically. This indicates that people would need a higher level of purpose in life to respond to the higher response categories. Thus, the PIL-SF items would be useful in determining people with a relatively high degree of purpose in life. Identifying people with different levels of purpose in life would allow them to be part of intervention programs, either to support those with low levels or to maintain and reinforce their purpose in life. The evidence of cross-country measurement invariance of the PIL-SF provides a measure to be used in cross-cultural studies about the meaning of life.

Network analysis of the relationships between conspiracy beliefs towards COVID-19 vaccine and symptoms of fear of COVID-19 in a sample of latin american countries.

The present study examined how conspiracy beliefs about COVID-19 vaccines specifically relate to symptoms of fear of COVID-19 in a sample of four South American countries. A total of 1785 people from Bolivia, Colombia, Ecuador, and Peru participated, responding to a sociodemographic survey, the Fear of COVID-19 scale (FCV-19 S) and the Vaccine Conspiracy Beliefs Scale-COVID-19 (VCBS-COVID-19). Network analysis identified the most important symptoms of fear and conspiracy beliefs about COVID-19 vaccines (nodes) and the associations between them (edges). In addition, the robustness of the network of these indicators of centrality and the possible differences in the structure and connectivity of the networks between the four countries were evaluated. The results suggest that the nodes with the highest centrality were items 2 and 5 of the FCV-19 S and item 2 of the VCBS-COVID-19. Likewise, item 6 is the belief that most predicts conspiracy beliefs about vaccines against COVID-19; while item 6 was the symptom that most predicts fear of COVID-19. The findings strongly support cross-cultural similarities in the networks across the four countries rather than differences. Although it was expected that a higher presence of symptoms of fear of COVID-19 may lead people to compensate for their fear by believing in conspiratorial ideas about vaccines and, consequently, rejecting the COVID-19 vaccine, the results do not clearly show this relationship. This could lead other researchers to generate evidence to explain the differences between Latin American countries and countries in other contexts in terms of vaccination rates. This evidence could be useful to develop policies favoring vaccination against COVID-19 that are more contextualized to the Latin American region, characterized by social instability and economic recession during the pandemic.