Molecular Systems Research Articles

Improved Gaussian basis sets for norm-conserving 4f-in-core pseudopotentials of trivalent lanthanides (Ln = Ce-Lu).

The first-principles quantum chemical computations often scale as Nk (N = basis sets; k = 1-4 for linear scaling, Hartree-Fock or density functional theory methods), which makes the development of accurate pseudopotentials and efficient basis sets necessary ingredients in modeling of heavy elements such as lanthanides and actinides. Recently, we have developed 4f-in-core norm-conserving pseudopotentials and associated basis sets for the trivalent lanthanides [Lu et al., J. Chem. Theory Comput. 19, 82-96 (2023)]. In the present paper, we present a unified approach to optimize high-quality Gaussian basis sets for modeling and simulations of condensed-phase systems. The newly generated basis sets not only capture the low total energy and fairly reasonable condition number of overlap matrix of lanthanide-containing systems, but also exhibit good transferability and reproducibility. These advantages ensure the accuracy of the basis sets while avoiding linear dependency concern of atom-centered basis sets. The performance of the basis sets is further illustrated in lanthanide molecular and condensed-phase systems by using Gaussian-plane wave density functional approach of CP2K. These new basis sets can be of particular interest to model structurally complicated lanthanide molecules, clusters, solutions, and solid systems.

Identifying Rare Events in Quantum Molecular Dynamics of Nanomaterials with Outlier Detection Indices.

Nanoscale and condensed matter systems evolve on multiple length- and time-scales, and rare events such as local phase transformation, ion segregation, defect migration, interface reconstruction, and grain boundary sliding can have a profound influence on material properties. We demonstrate how outlier detection indices can be used to identify rare events in machine-learning based, high-dimensional molecular dynamics (MD) simulations. Designed to order data-points from typical to untypical, the indices enable one to capture atomic events that are hard to detect otherwise. We demonstrate the approach with a nanosecond MD simulation of a grain boundary in a metal halide perovskite that is extensively studied for solar energy and optoelectronic applications. The method captures the initial grain boundary sliding and a spontaneous fluctuation half a nanosecond later, both events giving rise to persistent deep electronic trap states that impact charge carrier lifetime and transport and material performance. The approach offers a generalizable and simple method for identifying outlier events in complex condensed matter, molecular, and nanoscale systems.

DFT investigation of the adsorption and sensing ability of pure, Al and Ca-doped B12N12 nanocages toward thalidomide drug

In recent years, nanomaterials (NM) have been proven to be efficiency and applicable in several fields of our daily life, including in drug delivery systems. Herein, density functional theory (DFT) molecular modelling is used to probe the ability of pure, Al and Ca doped B12N12 fullerene-like nanocage for the detection and the sensing of thalidomide (TLD) drug. Thus, wB97XD/6-311G(d,p) level has been employed to study the structure-property relationship between the aforementioned NM and TLD. It turns from the analysis of thermodynamic parameters that all NMs investigated can spontaneously fix TLD at their external surface to form stable adsorbent-adsorbate molecular systems in standard conditions. The values of the Gibbs free adsorption energy for the most stable B12N12-TLD state is for instance found to be −8.97 and −6.98 kcal/mol in gas and aqueous phases respectively. QTAIM analyses, complemented by analyses of non covalent interactions through the reduced gradient density (RDG) revealed that the systems are further stabilized by a dative B-O bond as well as a strong N-H…N hydrogen bond between the adsorbent and the adsorbate. The HOMO-LUMO energy difference of CaB12N12 is found to reduced by about 36.42 % upon the adsorption of TLD, proving the interesting sensitivity of this NM toward the drug investigated. Finally, pure B12N12 and CaB12N12 are appropriate for the sensing of TLD in both gas phase and aqueous medium, the former NM even prensenting a release time of about 3.36 x 10−6 s in gas phase from the theoretical point of view.

Exploring chemical reactivity through a combined conceptual DFT and ELF topology approach.

In a proof-of-concept study, we explore how a combined approach using the topology of the electron localization function (ELF) and the condensed dual descriptor (DD) function can guide the optimal orientation between reactants and mimic the potential energy surfaces of molecular systems at the beginning of the chemical pathway. The DD has been chosen for its ability to evaluate the regioselectivity of neutral and soft species and to potentially mimic the interaction energy obtained from the mutual interactions between nucleophilic and electrophilic regions of the building blocks under perturbative theory. Our method has been illustrated with examples in which the optimal orientation of several systems can be successfully identified. The limitations of the presented model in predicting chemical reactivity are outlined in particular the influence of the selected condensation scheme.

A molecular comparison of [Fe-S] cluster-based homeostasis in Escherichia coli and Pseudomonas aeruginosa.

ISC and SUF molecular systems build and transfer Fe-S cluster to cellular apo protein clients. The model Escherichia coli has both ISC and SUF and study of the interplay between the two systems established that the ISC system is the house-keeping one and SUF the stress-responding one. Unexpectedly, our recent phylogenomic analysis revealed that in contrast to E. coli (and related enterobacteria such as Salmonella), most bacteria have only one system, and, in most cases, it is SUF. Pseudomonas aeruginosa fits the general rule of having only one system but stands against the rule by having ISC. This study aims at engineering P. aeruginosa harboring E. coli systems and vice versa. Comparison of the recombinants allowed to assess the functional versatility of each system while appreciating their contribution to cellular homeostasis in different species context.

A-280 The Good, the Bad, and the Downright Inappropriate reporting of Gram Positive Resistance Genes in a Proficiency Testing Program

Abstract Background Molecular technologies can rapidly identify the pathogen, allowing for the correct antimicrobial agent to be used for initial treatment. Many laboratories have implemented molecular technologies to identify the causative agents of infections from various bodily sources. Screening for potential resistance genes to common antimicrobial agents provides high value to clinicians to help guide optimal antimicrobial therapy selection. CLIA ‘88 required proficiency testing for physician office laboratories in 1994. This was in addition to the requirement for hospital and reference laboratories instituted under CLIA ‘67. This study compares the accuracy of laboratory performance for molecular multiplex platforms in the detection of Gram positive resistance genes from gold standard qualified samples in a novel proficiency program. Methods American Proficiency Institute, an independent proficiency testing provider, reviewed the reporting of various molecular multiplex systems against simulated patient samples in proficiency samples offered in 2023. Participants were instructed to only report resistance genes their systems were capable of testing for, and ensure they were appropriate for the organism recovered. Failure rates for participants were evaluated against an 80% participant consensus standard for detection of resistance genes. Results During the 2023 proficiency year, Gram positive targets were included 14 times in 4 different multiplex programs. Accuracy for correct reporting of resistance genes appropriate for the organism recovered ranged between 71.0%- 100%. Conversely, the number of laboratories reporting resistance genes that were inappropriate for the organism recovered ranged between 2.0% - 67.0%. Conclusions Based on the results reported by participants in API’s proficiency event, Molecular Multiplex platforms are accurately identifying Gram positive resistance genes. However, many participants using both FDA approved and LDT systems reported results for resistance genes that were not applicable for the organism recovered. This inaccurate reporting could potentially lead to inappropriate antimicrobial therapies being prescribed.

Multilevel Dynamic System as Molecular Morning-After Timer.

Systems chemistry aims to develop molecular systems that display emerging properties arising from their network and absent in their individual constituents. Employing reversible chemistry under thermodynamic control represents a valuable tool for generating dynamic combinatorial libraries of interconverting molecules, which may exhibit intriguing collective behaviour. A simple dynamic combinatorial library was prepared using dithioacetal / thiol / disulfide exchanges. Because of the relative reactivities of these reversible reactions, the library constitutes a two-layer dynamic system with one layer active in an acid medium (thiol/dithioacetal exchange) and one layer active in a basic medium (thiol/disulfide exchange). This property enables the system to respond to momentary changes in acidity of the medium by activating different network regions, channeling some building blocks from one layer to another through shared thiol reagents (nodes). This momentaneous change in wiring affects the final steady state composition of the library, measured the next day, even though the event that caused it vanishes without leaving any residue. Therefore, the final composition of this dynamic system provides information about this transient past perturbation in the environment such as: when it occurred, how long it was, or how intense it was.

Graph Neural Network-Based Molecular Property Prediction with Patch Aggregation.

Graph neural networks (GNNs) have emerged as powerful tools for quantum chemical property prediction, leveraging the inherent graph structure of molecular systems. GNNs depend on an edge-to-node aggregation mechanism for combining edge representations into node representations. Unfortunately, existing learnable edge-to-node aggregation methods substantially increase the number of parameters and, thus, the computational cost relative to simple sum aggregation. Worse, as we report here, they often fail to improve predictive accuracy. We therefore propose a novel learnable edge-to-node aggregation mechanism that aims to improve the accuracy and parameter efficiency of GNNs in predicting molecular properties. The new mechanism, called "patch aggregation", is inspired by the Multi-Head Attention and Mixture of Experts machine learning techniques. We have incorporated the patch aggregation method into the specialized, state-of-the-art GNN models SchNet, DimeNet++, SphereNet, TensorNet, and VisNet and show that patch aggregation consistently outperforms existing learnable and nonlearnable aggregation techniques (sum, multilayer perceptron, softmax, and set transformer aggregation) in the prediction of molecular properties such as QM9 thermodynamic properties and MD17 molecular dynamics trajectory energies and forces. We also find that patch aggregation not only improves prediction accuracy but also is parameter-efficient, making it an attractive option for practical applications for which computational resources are limited. Further, we show that Patch aggregation can be applied across different GNN models. Overall, Patch aggregation is a powerful edge-to-node aggregation mechanism that improves the accuracy of molecular property predictions by GNNs.

Measuring linear birefringence via rotating-sample transmission Stokes spectropolarimetry

Linear birefringence is a fundamental property of optically anisotropic media, defined by the difference in refractive index experienced by light polarized along orthogonal directions. It is usually manifested in microscopically aligned molecular systems, where a preferential direction of light–matter interaction is created. For instance, the anisotropic structure of calcite crystal causes the famous double-refraction phenomenon. Another common example is commercial adhesive tapes, which are polymeric materials possessing birefringent properties due to their manufacturing processes. The intrinsic relation between birefringence and molecular alignment forges a new analytical route to study materials such as polymeric thin films. Therefore, the capacity of measuring linear birefringence and its fast axis is of paramount importance for the science of anisotropic molecular systems. In this contribution, a comprehensive approach to acquire linear birefringence using rotating-sample transmission Stokes spectropolarimetry is presented and applied to transparent adhesive tapes as a case study. The experimental setup comprises a thermal light source and a spectropolarimeter capable of determining wavelength distributions of Stokes parameters. The samples are carefully aligned in a rotating mount and subjected to a fixed broadband vertically polarized light beam. Then, the transmitted light is analyzed using a rotating retarder type of spectropolarimeter. Through systematic variation of the sample’s angular position, the Stokes parameters of transmitted light are measured for each transmitted wavelength as a function of the sample’s angular position. The linear retardance and fast axis direction relative to the tape’s long axis are then determined from the modulation of Stokes parameters over sample rotation. The model derivation, experimental procedure, and signal processing protocol are described in detail, and the approach is verified with a simple correlation between linear retardance and the number of stacked layers of tape.

Development and validation of a new and rapid molecular diagnostic tool based on RT-LAMP for Hepatitis C virus detection at point-of-care

PurposeGlobally, it is estimated that 1.0 million individuals are newly infected by Hepatitis C virus (HCV) every year, and nearly 50 million people live with a chronic infection, according to World Health Organization. To overcome underdiagnosis of HCV infection among hard-to-reach populations, it is essential to develop new rapid and easy-to-use molecular diagnostic systems. In this work, we have developed a pangenotypic diagnostic tool based on Loop-Mediated Isothermal Amplification (LAMP), coupled to a direct sample lysis procedure for molecular detection of HCV at point-of-care (POC). MethodsProcedure validation was performed using 129 different samples from HCV infected patients (116 serum samples, and 13 fresh blood samples), 27 individuals who tested negative for HCV but positive for HIV, and 11 healthy donors. Serum was collected, lysed for 10 min at room temperature, and assayed by RT-LAMP. To achieve this, a set of 9 LAMP-primers was used for the first time. Parallel RT-qPCR assays were conducted for HCV to both validate the procedure and quantify viral loads. ResultsHCV was detected by RT-LAMP in 109/116 HCV positive serum samples, and in 11/13 positive blood samples in less than 40 min. Compared to RT-qPCR results, our RT-LAMP procedure showed a sensitivity of 94 %, 100 % specificity, and a limit of detection of 3.26 log10 IU/mL (10–20 copies per reaction). ConclusionsWe have developed an accurate system, more affordable than the current available rapid tests for HCV. Since no prior RNA purification step from capillary blood is required, we strongly recommend our RT-LAMP system as a valuable and rapid tool for the molecular detection of HCV at POC.

Accurate nuclear quantum statistics on machine-learned classical effective potentials.

The contribution of nuclear quantum effects (NQEs) to the properties of various hydrogen-bound systems, including biomolecules, is increasingly recognized. Despite the development of many acceleration techniques, the computational overhead of incorporating NQEs in complex systems is sizable, particularly at low temperatures. In this work, we leverage deep learning and multiscale coarse-graining techniques to mitigate the computational burden of path integral molecular dynamics (PIMD). In particular, we employ a machine-learned potential to accurately represent corrections to classical potentials, thereby significantly reducing the computational cost of simulating NQEs. We validate our approach using four distinct systems: Morse potential, Zundel cation, single water molecule, and bulk water. Our framework allows us to accurately compute position-dependent static properties, as demonstrated by the excellent agreement obtained between the machine-learned potential and computationally intensive PIMD calculations, even in the presence of strong NQEs. This approach opens the way to the development of transferable machine-learned potentials capable of accurately reproducing NQEs in a wide range of molecular systems.

Luminophores based on 2<i>H</i>-1,2,3-triazoles: synthesis, photophysical properties, and application prospects

The review systematizes the data on the synthesis of 2<i>H</i>-1,2,3-triazole-based luminophores published between 2005 and the beginning of 2024. The benefits and limitations of the described synthetic strategies are revealed. The possibilities and prospects for the practical applications of these molecular systems as chemosensors for pH and metal ion determination, bioimaging agents, and working elements of molecular electronic devices (organic light emitting diodes, organic field-effect transistors, <i>etc.</i>) are discussed. <br>The bibliography includes 84 references.

Multi-reference coupled cluster theory using the normal ordered exponential ansatz.

Properly spin-adapted coupled-cluster theory for general open-shell configurations remains an active area of research in electronic structure theory. In this contribution we examine Lindgren's normal-ordered exponential ansatz to correlate specific spin states using spin-free excitation operators, with the aid of automatic equation generation software. We present an intermediately normalised and size-extensive reformulation of the unlinked working equations, and analyse the performance of the method with single and double excitations for simple molecular systems in terms of accuracy and size-consistency.

ANI/EFP: Modeling Long-Range Interactions in ANI Neural Network with Effective Fragment Potentials.

Deep learning Neural Networks (NN) have been developed in the field of molecular modeling for the purpose of circumventing the high computational cost of quantum-mechanical calculations while rivaling their accuracies. Although these networks have found great success, they generally lack the ability to accurately describe long-range interactions, which makes them unusable for extended molecular systems. Herein, we provide a method for partially retraining the deep learning general-use neural network ANI, in which the long-range interactions are represented via atomic electrostatic potentials. The electrostatic potentials, generated with polarizable effective fragment potentials (EFP), are used as an additional input feature for the network. This new ANI/EFP network can predict solute-solvent interaction energies on a trained data set with a kcal/mol accuracy. It also shows promise in predicting the interaction energies of a solute in solvent environments that have not been included in a training data set. The proposed protocol can be taken as an example and further developed, leading to highly accurate and transferable neural network potentials capable of handling long-range interactions and extended molecular systems.

Subspace-Search Quantum Imaginary Time Evolution for Excited State Computations.

Quantum systems in excited states are attracting significant interest with the advent of noisy intermediate-scale quantum (NISQ) devices. While ground states of small molecular systems are typically explored using hybrid variational algorithms like the variational quantum eigensolver (VQE), the study of excited states has received much less attention, partly due to the absence of efficient algorithms. In this work, we introduce the subspace search quantum imaginary time evolution (SSQITE) method, which calculates excited states using quantum devices by integrating key elements of the subspace search variational quantum eigensolver (SSVQE) and the variational quantum imaginary time evolution (VarQITE) method. The effectiveness of SSQITE is demonstrated through calculations of low-lying excited states of benchmark model systems including H2 and LiH molecules. A toy Hamiltonian is also employed to demonstrate that the robustness of VarQITE in avoiding local minima extends to its use in excited state algorithms. With this robustness in avoiding local minima, SSQITE shows promise for advancing quantum computations of excited states across a wide range of applications.

Vibrational Fermi Resonance in Atomically Thin Black Phosphorus.

Fermi resonance is a phenomenon involving the hybridization of two coincidentally quasi-degenerate states that is observed in the vibrational or electronic spectra of molecules. Despite numerous examples in molecular systems, vibrational Fermi resonances in dispersive semiconducting systems remain largely unexplored due to the rarity of occurrence. Here we report a vibrational Fermi resonance in atomically thin black phosphorus. The Fermi resonance arises via anharmonic mixing of a fundamental Raman mode and a Davydov component of an infrared mode, leading to a doublet with mixed character. The extent of Fermi coupling can be modulated by the application of external biaxial strain. The consequences of Fermi hybridization are revealed by electronic resonance effects in the thickness-dependent and excitation-wavelength-dependent Raman spectrum, which is predicted by ab initio hybrid functional simulations including excitonic interactions. This work reveals new insight into electron-phonon coupling in black phosphorus and demonstrates a novel method for modulating Fermi resonances in 2D semiconductors.

Synthesis of metal(II) acetate complexes with a heterocyclic azo dye ligand: Characterization, geometrical molecular structures, antioxidant, biological activities, DNA binding and molecular docking studies

A series of Cu(II), Co(II), Ni(II), Mn(II), and UO2(II) complexes have been prepared from the ligand 4-(2’,4’-dihydroxybenzaldehyde phenylazo)antipyrine (H2L). FT-IR spectra demonstrated that the ligand under investigation behaves as a bi-and tridentate centre ligand through the phenolic oxygen of aldehydic, azo nitrogen atoms and carbonyl oxygen of antipyrine and forms a stable five- and six-membered chelating ring. From the X-ray crystallographic analysis (XRD) patterns of the ligand and complexes showed the polycrystalline phases are indicated by the multiple diffraction peaks in the 1igand and its complex (5). The HOMO‒LUMO energy gaps (∆E) were used to create theoretical models for the structure and confirmation barriers in molecular systems for the ligand (H2L) and metal complexes. It was found that the Cu(II), Co(II), Ni(II), and Mn(II) complexes have lower values for HOMO‒LUMO energy gaps (∆E), indicating that they are more stable than the 1igand. The biological activities of ligand (H2L) and its metal complexes showed that ligand was stronger antimicrobial and antioxidant agent than its metal complexes. 50 µg/ml H2L caused inhibition zones ranged from 13 to 20 mm against the tested bacteria and 13 to 16 against fungi. In addition, the present study tested metal (II) acetate complexes with a heterocyclic azodye ligand for their application in the textile industry as antimicrobial dyed fabrics. All obtained results confirmed the possibility of using the prepared compounds in different industrial applications. The 1igand (H2L) and its metal complexes showed moderate to strong antimicrobial activities in agar well diffusion, MIC, and antioxidant tests. Ligand and its metal complexes revealed inhibition zones against all tested microbes ranged between 6 to 24 mm. The MIC values of ligand and its metal complexes were recorded at 20 to 30 μg/ml and 30 to 145 μg/ml, respectively. 50 µg/ml of 1igand and its metal complexes was enough to complete inhibit Gram negative E. coli moreover they could inhibit the multidrug resistant bacteria K. pneumoniae and Pseudomonas sp. By molecular docking study, it was found that the complex (1) showed the highest and better binding affinity (-8.38874 and -7.74442 kcal/mol) than other 1igand (H2L) and complexes (2–5) for the breast cancer (3HB5) protein central activity and the colon cancer (2HQ6) protein central activity.

Влияние физической нагрузки различной интенсивности на когнитивные способности студентов

There is increasing evidence that physical activity positively affects cognitive function. Physical exercise activates molecular systems involved in metabolism and synaptic plasticity, and the interaction between these systems has been shown to influence cognitive function. The challenge for the scientific community is to find an optimal level of physical activity for students that will promote harmonious development and enhance the effectiveness of their academic performance. This article highlights the influence of various intensities of physical activity on the dynamics of intelligence indicators in male and female students. In the process of the pedagogical experiment, the students were distributed into two groups according to a certain mode of physical activity. The classes in the experimental group included exercises of high intensity, while the classes in the control group included mainly exercises of low intensity. Intellectual abilities were measured before and after 2 months of the pedagogical experiment. The Raven's Standardized Performance Matrices (SPM) test was used to assess intellectual abilities. The results of the study showed that the increase in intelligence indicators in the students of the experimental group is statistically significant, in contrast to the control group. At the same time, it was not possible to identify significant differences using ANOVA. The obtained data are useful in the context of further research, as they show specialists engaged in the study and development of the cognitive abilities of students, different possibilities of influence and a certain range in the organization of further.

Toward Polaritonic Molecular Orbitals for Large Molecular Systems.

A comprehensive understanding of electron-photon correlation is essential for describing the reshaping of molecular orbitals in quantum electrodynamics (QED) environments. The strong coupling QED Hartree-Fock (SC-QED-HF) theory tackles these aspects by providing consistent molecular orbitals in the strong coupling regime. The previous implementation, however, has significant convergence issues that limit the applicability. In this work, we introduce two second-order algorithms that significantly reduce the computational requirements, thereby enhancing the modeling of large molecular systems in QED environments. Furthermore, the implementation will enable the development of correlated methods based on a reliable molecular orbital framework as well as multi-level methodologies able to model the inclusion of solvent effects in this kind of complex systems.

Palladium-immobilized triazine dendrimer on magnetic nanoparticles: as reusable microreactor for solvent-dependent synthesis strategy of 2,3-diphenylindoles and pentaphenylpyrrole derivatives

In this work, we demonstrate that palladium-immobilized triazine dendrimer on magnetic nanoparticles in proper solvents, provides an impressive, atom-economical and compelling approach for the selective synthesis of 2,3-diphenylindole or pentaphenylpyrrole derivatives via annulation of diphenylacetylene with diverse anilines. Both the annulation methods were taken place under copper- and phosphine-free conditions with high yields at air atmosphere. Likewise, bis-indoles were obtained with excellent yields under optimized reaction conditions. Besides, the catalyst was isolated and reused for seven cycles without decrease potential of catalytic activity. Two mechanistic pathways were proposed and geometry optimizations, electronic properties as well as vibrational characterizations of all structures were performed with density functional theory (DFT). Also, the investigation of atomic basin properties of these molecular systems was carried out utilizing the quantum atoms-in-molecules theory (QTAIM). The results showed that 2,3-diphenylindole and pentaphenyl pyrrole molecular systems can be used as intramolecular acceptor/donor (n-like/p-like) sections.