Search Results (137)

Understanding the intrinsic Li–H₂ interaction, decoupled from substrate effects, is essential to rationalize the performance of lithium-decorated hydrogen storage materials. To address the current lack of a clean theoretical baseline, we characterized the sequential H₂ adsorption on the gas-phase Li₆ superatomic cluster using high-level density functional theory (DFT), complemented by Energy Decomposition Analysis (EDA), QTAIM, and NICS(0) calculations. Li₆ acts as a structurally rigid platform (RMSD < 0.032 Å) where ligand-induced polarization progressively strengthens its σ-aromaticity (NICS(0) from −2.917 to −13.98 ppm) and increases the HOMO–LUMO gap up to 5.05 eV. EDA identifies the binding as field-activated physisorption, electrostatically dominated (65–67%) and mechanistically distinct from Kubas coordination, as confirmed by QTAIM closed-shell interaction parameters. Negative cooperativity governs an effective loading capacity of n = 2 molecules under cryogenic conditions (T_eq = 143.76 and 114.64 K), while an entropic bottleneck renders higher loading non-spontaneous at all temperatures. These results establish Li₆(H₂)_n as a foundational gas-phase reference, providing a systematic, contamination-free descriptor set for the intrinsic Li–H₂ interaction. This framework is essential for isolating the electronic role of the lithium superatom and unambiguously identifying substrate-induced modulations in supported hydrogen storage materials. Full article

Ab initio studies using CASSCF/MRCI calculations have been performed to investigate the spectroscopic properties of YLi and ScLi molecules. Our calculations have computed 25 singlet and triplet states for YLi and 37 electronic states for ScLi. The lowest lying states, including the ground state ¹∑⁺ of YLi, have been investigated for the first time. The spin–orbit coupling in YLi has also been assessed from the splitting between Ω components generated from the lowest triplet lying Λ–S states. Regarding ScLi, the ground state is found to be the (1)³Δ state. Spectroscopic constants, energy levels at equilibrium, permanent dipole moments, and transition dipole moments have also been calculated. The potential energy curves for all calculated states have been displayed to large bond internuclear distances. In both ScLi and YLi, the potential energy curves have shown a small dissociation energy for the lowest states (1) ^1,3Δ, (1) ^1,3Π and (1) ^1,3∑⁺. Full article

In this research article, a silicon carbide (SiC) nanocluster has been designed and characterized as an anode electrode for lithium (Li), sodium (Na), potassium (K), beryllium (Be), magnesium (Mg), boron (B), aluminum (Al) and gallium (Ga)-ion batteries through the formation of SiLiC, SiNaC, SiKC, SiBeC, SiMgC, SiBC, SiAlC and SiGaC nanoclusters. A vast study on energy-saving by SiLiC, SiNaC, SiKC, SiBeC, SiMgC, SiBC, SiAlC and SiGaC complexes was probed using computational approaches accompanying density state analysis of charge density differences (CDDs), total density of states (TDOS) and molecular electrostatic potential (ESP) for hybrid clusters of SiLiC, SiNaC, SiKC, SiBeC, SiMgC, SiBC, SiAlC and SiGaC. The functionalization of Li, Na, K, Be, Mg, B, Al and Ga metal/metalloid elements can raise the negative charge distribution of carbon elements as electron acceptors in SiLiC, SiNaC, SiKC, SiBeC, SiMgC, SiBC, SiAlC and SiGaC nanoclusters. Higher Si/C content can increase battery capacity through SiLiC, SiNaC, SiKC, SiBeC, SiMgC, SiBC, SiAlC and SiGaC nanoclusters for energy storage processes and to improve the rate performance by enhancing electrical conductivity. Full article

Understanding and controlling thermal rectification is pivotal for designing phononic devices that guide heat flow in a preferential direction. This study investigates one-dimensional atomic chains with binary mass asymmetry and nonlinear interatomic potentials, focusing on how energy propagates under thermal and wave excitation. Two potential models—the β-FPU and Morse potentials—were employed to examine the role of nonlinearity and bond softness in energy transport. Simulations reveal strong directional energy transport governed by the interplay of mass distribution, nonlinearity, and excitation type. In FPU chains, pronounced rectification occurs: under “cold-heavy” conditions, energy in the left segment increases from ~1% to over 63%, while reverse (“hot-heavy”) cases show less than 4% net transfer. For wave-driven excitation, the rectification coefficient reaches ~0.58 at 100:1. In contrast, Morse-based systems exhibit weaker rectification (∆E < 1%) and structural instabilities at high asymmetry due to bond breaking. A comprehensive summary and heatmap visualization highlight how system parameters govern rectification efficiency. These findings provide mechanistic insights into nonreciprocal energy transport in nonlinear lattices and offer design principles for nanoscale thermal management strategies based on controlled asymmetry and potential engineering. Full article

Machine learning models and web-based tools have been developed for predicting key properties of imidazolium-based ionic liquids. Two high-quality datasets containing experimental density and viscosity values at 298 K were curated from the ILThermo database: one containing 434 systems for density and another with 293 systems for viscosity. Molecular structures were optimized using the GOAT procedure at the GFN-FF level to ensure chemically realistic geometries, and a diverse set of molecular descriptors, including electronic, topological, geometric, and thermodynamic properties, was calculated. Three support vector regression models were built: two for density (IonIL-IM-D1 and IonIL-IM-D2) and one for viscosity (IonIL-IM-V). IonIL-IM-D1 uses three simple descriptors, IonIL-IM-D2 improves accuracy with seven, and IonIL-IM-V employs nine descriptors, including DFT-based features. These models, designed to predict the mentioned properties at room temperature (298 K), are implemented as interactive applications on the atomistica.online platform, enabling property prediction without coding or retraining. The platform also includes a structure generator and searchable databases of optimized structures and descriptors. All tools and datasets are freely available for academic use via the official web site of the atomistica.online platform, supporting open science and data-driven research in molecular design. Full article

Inhibitors of the tyrosine kinase Zap70 are actively searched to improve treatments of lymphoid malignancies and autoimmune diseases associated with an abnormal T-cell response. The natural product withaferin A (WFA) has been characterized as a covalent inhibitor of Zap70 capable of blocking the migration of human T-cells. By analogy, we postulated that other withanolides equipped with a thiol-reactive, α,β-unsaturated ketone may form covalent complexes with Zap70. The hypothesis was tested using a molecular modeling approach with a panel of 12 withanolides docked onto the kinase domain of Zap70. Seven natural products revealed a capability to form stable complexes with Zap70 comparable to that of WFA, including withangulatin A, 4β-hydroxywithanolide E, withaperuvin, and ixocarpalactone A. Withangulatin A surpassed all the other withanolides for its ability to engage an interaction with Zap70 kinase and to form covalent complexes via bonding to the Cys346 residue close to the enzyme active site. The physicochemical and ADMET properties of withangulatin A were analyzed via Density Functional Theory calculations and an analysis of its Fukui function descriptors. The C3 position of the enone moiety was identified as the most reactive (nucleophilic) site of the molecule. Withangulatin A revealed a satisfactory ADMET profile with no major toxicity anticipated. It represents a potential hit to guide the design of Zap70 inhibitors. Full article

Phytoecdysteroids represent a class of naturally occurring substances known for their diverse biological functions, particularly their strong ability to stimulate protein anabolism. In this study, a computational machine learning-driven quantitative structure–activity relationship (QSAR) approach was applied to analyze the anabolic potential of 23 ecdysteroid compounds. The ML-based QSAR modeling was conducted using a combined approach that integrates Genetic Algorithm-based feature selection with Multiple Linear Regression Analysis (GA-MLRA). Additionally, structure optimization by semi-empirical quantum-chemical method was employed to determine the most stable molecular conformations and to calculate an additional set of structural and electronic descriptors. The most effective QSAR models for describing the anabolic activity of the investigated ecdysteroids were developed and validated. The proposed best model demonstrates both strong statistical relevance and high predictive performance. The predictive performance of the resulting models was confirmed by an external test set based on R²_test values, which were within the range of 0.89 to 0.97. Full article

Divinylisoprene rubber, a copolymer of butadiene and isoprene, is used as raw material for rubber technical products, combining isoprene rubber’s elasticity and butadiene rubber’s wear resistance. These properties depend quantitatively on the copolymer composition, which depends on the kinetics of its synthesis. This work aims to theoretically describe how the monomer mixture composition in the butadiene–isoprene copolymerization affects the activity of the TiCl₄-Al(i-C₄H₉)₃ catalytic system (expressed by active sites concentration) via kinetic modeling. This enables development of a reliable kinetic model for divinylisoprene rubber synthesis, predicting reaction rate, molecular weight, and composition, applicable to reactor design and process intensification. Active sites concentrations were calculated from experimental copolymerization rates and known chain propagation constants for various monomer compositions. Kinetic equations for active sites formation were based on mass-action law and Langmuir monomolecular adsorption theory. An analytical equation relating active sites concentration to monomer composition was derived, analyzed, and optimized with experimental data. The results show that monomer composition’s influence on active sites concentration is well described by a two-step kinetic model (physical adsorption followed by Ti–C bond formation), accounting for competitive adsorption: isoprene adsorbs more readily, while butadiene forms more stable active sites. Full article

Recent research on the design and synthesis of new and upgraded materials for secondary batteries is growing to fulfill future energy demands around the globe. Herein, by using DFT calculations, the thermodynamic and electrochemical properties of Na/Na⁺@C₁₂ complexes and then halogens (X⁻ = Br⁻, Cl⁻, and F⁻) as counter anions are studied for the enhancement of Na-ion battery cell voltage and overall performance. Isolated C₁₂ nanorings showed a lower cell voltage (−1.32 V), which was significantly increased after adsorption with halide anions as counter anions. Adsorption of halides increased the Gibbs free energy, which in turn resulted in higher cell voltage. Cell voltage increased with the increasing electronegativity of the halide anion. The Gibbs free energy of Br⁻@C₁₂ was −52.36 kcal·mol⁻¹, corresponding to a desirable cell voltage of 2.27 V, making it suitable for use as an anode in sodium-ion batteries. The estimated cell voltage of these considered complexes ensures the effective use of these complexes in sodium-ion secondary batteries. Full article

This study employs density functional theory (DFT) to investigate the electronic and optical properties of molybdenum (Mo) and chalcogen (S, Se, Te) co-doped anatase TiO₂. Two co-doping configurations were examined: Model 1, where the dopants are adjacent, and Model 2, where the dopants are farther apart. The incorporation of Mo into anatase TiO₂ resulted in a significant bandgap reduction, lowering it from 3.22 eV (pure TiO₂) to range of 2.52–0.68 eV, depending on the specific doping model. The introduction of Mo-4d states below the conduction band led to a shift in the Fermi level from the top of the valence band to the bottom of the conduction band, confirming the n-type doping characteristics of Mo in TiO₂. Chalcogen doping introduced isolated electronic states from Te-5p, S-3p, and Se-4p located above the valence band maximum, further reducing the bandgap. Among the examined configurations, Mo–S co-doping in Model 1 exhibited most optimal structural stability structure with the fewer impurity states, enhancing photocatalytic efficiency by reducing charge recombination. With the exception of Mo–Te co-doping, all co-doped systems demonstrated strong oxidation power under visible light, making Mo-S and Mo-Se co-doped TiO₂ promising candidates for oxidation-driven photocatalysis. However, their limited reduction ability suggests they may be less suitable for water-splitting applications. The study also revealed that dopant positioning significantly influences charge transfer and optoelectronic properties. Model 1 favored localized electron density and weaker magnetization, while Model 2 exhibited delocalized charge density and stronger magnetization. These findings underscore the critical role of dopant arrangement in optimizing TiO₂-based photocatalysts for solar energy applications. Full article

Machine learning (ML) is transforming computational chemistry by accelerating molecular simulations, property prediction, and inverse design. Central to this transformation is mathematical optimization, which underpins nearly every stage of model development, from training neural networks and tuning hyperparameters to navigating chemical space for molecular discovery. This review presents a structured overview of optimization techniques used in ML for computational chemistry, including gradient-based methods (e.g., SGD and Adam), probabilistic approaches (e.g., Monte Carlo sampling and Bayesian optimization), and spectral methods. We classify optimization targets into model parameter optimization, hyperparameter selection, and molecular optimization and analyze their application across supervised, unsupervised, and reinforcement learning frameworks. Additionally, we examine key challenges such as data scarcity, limited generalization, and computational cost, outlining how mathematical strategies like active learning, meta-learning, and hybrid physics-informed models can address these issues. By bridging optimization methodology with domain-specific challenges, this review highlights how tailored optimization strategies enhance the accuracy, efficiency, and scalability of ML models in computational chemistry. Full article

CrystalShift is an open-source computational tool tailored for the analysis, transformation, and conversion of crystallographic data, with a particular emphasis on organic crystal structures. It offers a comprehensive suite of features valuable for the computational study of solids: format conversion, crystallographic basis transformation, atomic coordinate editing, and molecular layer analysis. These options are especially valuable for studying the mechanical properties of molecular crystals with potential applications in organic materials science. Written in the C programming language, CrystalShift offers computational efficiency and compatibility with widely used crystallographic formats such as CIF, POSCAR, and XYZ. It provides a command-line interface, enabling seamless integration into research workflows while addressing specific challenges in crystallography, such as handling non-standard file formats and robust error correction. CrystalShift may be applied for both in-depth study of particular crystal structure origins and the high-throughput conversion of crystallographic datasets prior to DFT calculations with periodic boundary conditions using VASP code. Full article

Kapakahines A–G are natural products isolated from the marine sponge Carteriospongia sp., characterized by complex molecular architectures composed of fused rings and diverse functional groups. Preliminary studies have indicated that some of these peptides may exhibit cytotoxic and antitumor activities, which has prompted interest in further exploring their chemical and pharmacokinetic properties. Computational chemistry—particularly Conceptual Density Functional Theory (CDFT)-based Computational Peptidology (CP)—offers a valuable framework for investigating such compounds. In this study, the CDFT-CP approach is applied to analyze the structural and electronic properties of Kapakahines A–G. Alongside the calculation of global and local reactivity descriptors, predicted ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiles and pharmacokinetic parameters, including pKa and LogP, are evaluated. The integrated computational analysis provides insights into the stability, reactivity, and potential drug-like behavior of these marine-derived cyclopeptides and contributes to the theoretical groundwork for future studies aimed at optimizing their bioactivity and safety profiles. Full article

This paper reports on a theoretical investigation of the bond dissociation energies of B–H and B–F interactions of closo-borate anions [B_nH_n−1X]²⁻ (n = 6, 10 and 12; X = H and F), in which homolytic and heterolytic bond breaking cases were considered, and the main trends in bond dissociation energy values were analysed. The wB97X-D3/TZVPP level of theory was applied for geometry optimisation of the molecular species under consideration. DLPNO-CCSDT/CBS single-point calculations were made to ensure an accurate estimation of the target systems’ electronic energy. The correlations between the value of the bond dissociation energy and variables such as electron density descriptors of B–H and B–F interactions and frontier orbital energies (HOMO, SOMO and LUMO) were established. Full article

Asymmetric cove-edged graphene nano-ribbons were employed as metallic electrodes in a hybrid gap device structure with zig-zag graphene nano-ribbons terminations for amino acid recognition and peptide sequencing. On a theoretical basis, amino acid recognition is attained by calculating, using the non equilibrium Green function scheme based on density functional theory, the transversal tunnelling current flowing across the gap device during the peptide translocation through the device. The reliability and robustness of this sequencing method versus relevant operations parameters, such as the bias, the gap size, and small perturbations of the atomistic structures, are studied for the paradigmatic case of Pro-Ser model peptide. I evidence that the main features of the tunnelling signal, that allow the recognition, survive for all of the operational conditions explored. I also evidence a sort of geometrical selective sensitivity of the hybrid cove-edged graphene nano-ribbons versus the bias that should be carefully considered for recognition. Full article