Abstract:

This computational study investigates the thermal decomposition of 1,2,4-triazol-3(2H)-ones and their thione analogues using Density Functional Theory (DFT). The reaction proceeds via a concerted, six-membered cyclic transition state, primarily driven by the breaking of the N–N bond. A key finding is that the accuracy of the calculated activation energies (Ea) strongly depends on the choice of the DFT functional. For sulfur-containing systems (thiones), the hybrid functional APFD (with 25% Hartree-Fock exchange) provides the most reliable results, effectively describing their higher polarizability. In contrast, for oxygen-containing systems (triazolones), the dispersion-corrected functional B97D-GD3BJ (with 0% Hartree-Fock exchange) delivers superior accuracy by better modeling electrostatic and dispersion interactions. The -CH₂CH₂CN group at the N-2 position acts not only as a protecting group, but also stabilizes the transition state through non-covalent interactions. Electron-withdrawing substituents slightly increase the Ea, while electron-donating groups decrease it. Sulfur analogues consistently show significantly lower activation energies (by ~40 kJ/mol) than their oxygen counterparts, explaining their experimentally observed faster decomposition. This work establishes a dual-methodology computational framework for accurately predicting the kinetics of these reactions, providing valuable insights for the regioselective synthesis of biologically relevant triazole derivatives via controlled pyrolysis.

Abstract:

Tuberculosis, caused by Mycobacterium tuberculosis (M. tb), remains a leading global threat, escalated now by the rise of multidrug-resistant (MDR-TB) and extensively drug-resistant (XDR-TB) strains. In search of a novel anti-tubercular agent with a distinct mechanism of action, this study explores deep-sea marine metabolites as potential inhibitors of the F₄₂₀-dependent oxidoreductase Rv1155, a redox enzyme essential for M. tb survival. A total of 2,773 marine-derived compounds curated from the CMNPD, Reaxys, and MarinLit databases were screened using an integrated CADD workflow combining molecular docking, in-silico ADMET profiling, and molecular dynamics (MD) simulations. Docking results revealed several metabolites with high affinity for the Rv1155 binding pocket, and three compounds: Upenamide (CMNPD_22964), Aspyronol (Compound_1749), and Fiscpropionate F (Compound_1796) as hit candidates. Among these, Upenamide displayed the strongest binding and stable protein-ligand dynamics, while Aspyronol demonstrated a promising ADMET profile comparable to that of the native cofactor F₄₂₀₂. These findings highlight the potential of deep-sea marine metabolites as a valuable source of anti-tubercular scaffolds and establish a computationally driven, cost-effective framework for discovering inhibitors targeting F₄₂₀-dependent enzymes. This approach provides a foundation for future experimental validation and expansion to additional F₄₂₀-related drug targets in M. tb.

Abstract:

The reactions of formaldehyde oxide (CH2OO) with methane and ethane that yield alcohol products were investigated using dual-level variational transition state theory with multidimensional tunneling corrections (VTST/MT). Additional systems—including halogenated formaldehyde oxides (CF2OO and CCl2OO), deuterated alkanes (CD4, C2D6), and isotopically substituted formaldehyde oxide (CH218O18O)—were also examined to explore substituent and isotope effects. Bimolecular rate constants and kinetic isotope effects (KIEs) were computed over the temperature range of 100–600 K. Significant tunneling contributions were predicted, especially below room temperature, where tunneling increases the rate constants of the CH2OO + alkane reactions by up to two orders of magnitude. The computed H/D KIEs are approximately 3 at 300 K and rise to ~10 at 200 K. Notably, pronounced oxygen tunneling was also observed, giving 18O KIEs of ~1.2 at 300 K and ~2.2 at 200 K. Halogen substitution was predicted to substantially reduce reaction barriers due to the weakening of the O–O bond, leading to rate constants for CF2OO reactions that exceed those of CH2OO by more than ten orders of magnitude at 300 K. The mechanisms underlying the strong tunneling effects, the individual contributions to the calculated KIEs, and the implications of these findings for atmospheric chemistry are discussed.

Abstract: Due to their chemical structure and convenient, tunable physicochemical properties, intermetallic alloys and materials are promising for use in various fields, such as gas sensors. The aim of this study was to perform DFT GGA calculations of the adsorption energy of atomic nitrogen and the properties of partially substituted metal atoms with nitrogen in Ti3Sb supercells with an A15 cubic structure. Adsorption and doping at various adsorption sites and crystallographic orientations (110; 111; 100), as well as their electronic properties, were studied in 2 × 2, 3 × 3, and 5 × 5 supercells. The density of states (DOS) of Ti3Sb–N supercells with two different positions of partial substitution of nitrogen for Ti3Sb metal atoms was calculated: N/Ti and N/Sb. Comparative data show that the structural and energetic properties of Ti3Sb–N vary compared to pure Ti3Sb. Controlled incorporation of nitrogen atoms and partial substitution of Ti3Sb atoms allow for tuning of the properties of Ti3Sb–N. These data can be used to optimize and predict the electronic structure and response characteristics of such materials for electronics and catalysis. They are also important as potential sensor materials with exceptional properties and promising applications for nitrogen detection in targeted developments.

Abstract: Electronegativity is a cornerstone of chemical theory, yet its traditional definitions remain empirical. Here, a variational definition of electronegativity (\( χ_V \)χ_V) is proposed, derived directly from the principle of least action. In this framework, the ratio between the effective nuclear charge (Z_eff\( Z_eff \)) and the principal quantum number (\( n^* \)n*) quantifies the deviation of an atom from its stationary (minimal-action) configuration, yielding \( \) χ_V=κ[(Z_eff/n* )²-1] , with a single universal constant (κ) fixed using fluorine. The scale reproduces periodic trends without empirical fitting and shows strong linear correlations with Pauling, Mulliken, and Allen electronegativities (r = 0.91–0.97, R² ≈ 0.9). χ_V also exhibits a nearly perfect proportionality with the first ionization energy (r = 0.9999) and an inverse-square dependence on atomic radius (R² = 0.904). When applied to 47 diatomic molecules, predicted bond-dissociation energies yield a mean absolute error of 15.8 kJ mol⁻¹—slightly lower than Pauling’s classical relation. These results demonstrate that electronegativity can be interpreted as a quantified deviation from the condition of least action, bridging atomic structure, energy, and reactivity within a unified physical framework.

Abstract: A unified electromagnetic response theory has been formulated in terms of quasi-energy derivatives within the nonrelativistic single-determinant framework. The formalism is applicable to both optical and non-optical electromagnetic responses, without restriction to monochromatic fields. Electromagnetic properties are expressed through quasi-energy derivatives, providing a consistent and general description under arbitrary static or dynamic perturbations. Magnetic properties obtained from this framework are inherently gauge-invariant, since a gauge transformation of the electromagnetic potentials corresponds to a unitary phase transformation acting on both the Hamiltonian and molecular orbitals. The present theory thus offers a comprehensive foundation for evaluating (hyper)polarizabilities, (hyper)magnetizabilities, and other related response properties.

Abstract: Machine learning is rapidly reshaping the field of chemical synthesis, influencing how chemists design, plan, and carry out reactions. Yet for many students, the role of artificial intelligence in synthesis remains abstract or inaccessible. This article introduces the current landscape of machine learning in chemistry, focusing on why these new approaches are emerging and how they complement, rather than replace, human creativity. We outline the challenges of traditional retrosynthetic planning, describe the rise of AI-driven platforms such as IBM RXN and AiZynthFinder, and discuss how they exemplify broader changes in the practice of chemistry. Practical “try it yourself” suggestions are included to help students and educators explore these developments firsthand. By highlighting both opportunities and limitations, this guide equips learners to critically understand the future of synthesis as a collaboration between chemists and computational tools.

Abstract: Every chemist knows the small heartbreak: the calculation looks beautiful; the flask does not. This paper takes that feeling seriously and names it—the reality gap—and then shows how to cross it on purpose. Our thesis is straightforward: predictive chemistry emerges when we let theory and experiment argue in public, with machine learning acting as the translator that keeps the debate honest.We first map where neat first‑principles wobble in the wild: bonds stretching and breaking, surfaces choosing a pathway, solvents shifting free energies just enough to matter, and spin states reorganizing the landscape. We then show how to correct those edges without discarding physics: hybrid QM/ML methods that learn systematic errors, uncertainty that travels with every prediction so we know when to trust and when to measure, and chemistry‑aware transfer learning so models trained on idealized inputs remain useful on real instruments.The loop closes when models talk to tools and tools talk back. Process‑analytical technologies feed real‑time signals to Bayesian optimization, multi‑objective workflows make trade‑offs visible (yield, selectivity, cost, greenness), and autonomy becomes conditional by design—robots execute, chemists steer. We focus on validation that survives deployment, not convenience: splits that reflect how chemistry varies in practice, calibrated confidence, and structured logging that treats failures as first‑class data. Finally, we detail what this buys in real laboratories: faster cycles, reproducible and information‑rich datasets, greener routes—and decisions made with eyes open.The message is practical and hopeful. Keep the physics where it is strong. Teach it where it is stubbornly wrong. Carry uncertainty forward. Let instruments help decide where to look next. Do that, and the calculation and the flask still won’t always agree—but they will disagree productively, more often, and for reasons we can understand. That is predictive chemistry in practice.

Abstract: Chemical bonds among carbon are central to Chemistry. A general working principle regarding these interactions is that these contacts get stronger as the carbon atoms become closer to each other. Nevertheless, there are long, yet strong single C–C bonds which challenge this interpretation. Herein, we perform a quantitative thorough decomposition of the electronic energy of hexaphenylethane and several derivatives of this molecule with increasingly bulkier substituents. For this purpose, we exploited state-of-the-art methods of wave function analysis for the examination of the chemical bonding scenario in the examined systems, namely the Quantum Theory of Atoms in Molecules (QTAIM) and the Interacting Quantum Atoms (IQA) electronic energy partition. Our results reveal the predominance of collective non-covalent interactions over the central, covalent one in the chemical bonding of the examined molecules, in particular for those which have been synthesised in the laboratory. The QTAIM and IQA methods also showed that besides London dispersion, electron sharing comprises and important contribution to the above mentioned collective interactions. Overall, our results give valuable insights about the importance of collective interactions in the investigated systems and they aid in the understanding of the nature or long, yet stable single C–C bonds.

Abstract: The revolutionary field of artificial intelligence (AI) has affected all aspects of our lives, including the field of chemistry. The impact of AI has been felt even more strongly in recent years, as new powerful computational tools have emerged. This review looks at the evolution of AI in chemistry, from a not-too-distant past when AI was limited to rule-based systems and simulation for simple data analyses to today's world of advanced (or powerful) machine learning. Despite the name, "advanced machine learning" refers to a highly diverse family of AI systems—most of which are not learned in the way, or with the types of data, that humans typically use to understand the world. The latest advances in AI, particularly deep learning, hold the promise of revolutionizing chemistry. The use of these advanced computational methods enables researchers to extract relationships from large datasets of molecular and chemical information. This capacity to discern pattern recognition within big data allows for the accurate prediction of molecular properties. It furthermore enables the efficient optimization of chemical reactions and the design of new materials at the molecular scale. Whether directly applied to chemistry or harnessed through interdisciplinary collaboration, AI will make a significant impact on the pace of chemical research in coming years. The review article highlights these prospects and discusses some specific areas, including drug discovery and materials science, where AI's impact will likely be felt most keenly. Moreover, AI is beginning to find applications in chemical synthesis and spectroscopy, and these have made predictions about reactions easier and enhanced data interpretation spectacularly. One of the promises going forward is the hoped-for emergence of quantum computing, which may take the not-yet-fully-realized uses of AI in chemistry even further. One of the main points made in this review is that for all this to happen, the chemistry community may need to plan a bit; interdisciplinary collaborations between chemists and AI experts will be essential to push forward the kinds of uses AI could have in our field.

Abstract: Carbon dioxide is naturally present in the Earth’s atmosphere and plays a role in regulat-ing and balancing the planet's temperature. However, due to various human activities, the amount of carbon dioxide is increasing beyond safe limits, disrupting the Earth's natural temperature regulation system. Today, CO₂ is the most prevalent greenhouse gas; as its concentration rises, significant climate change occurs. Therefore, there is a need to utilise anthropogenically released carbon dioxide in valuable fuels, such as formic acid (HCOOH). Single-atom catalysts are widely used, where a single metal atom is anchored on a surface to catalyse chemical reactions. In this study, we investigated the potential of Cu@Phosphorene as a single-atom catalyst (SAC) for CO₂ reduction using quantum chemical calculations. All computations for Cu@Phosphorene were performed using density functional theory (DFT). Mechanistic studies were conducted for both bimolecular and termolecular pathways. The bimolecular mechanism involves one CO₂ and one H₂ molecule adsorbing on the surface, while the termolecular mechanism involves two CO₂ molecules adsorbing first, followed by H₂. Re-sults indicate that the termolecular mechanism is preferred for formic acid formation due to its lower activation energy. Further analysis included charge transfer assessment via NBO, and interactions between the substrate, phosphorene, and the Cu atom were confirmed using quantum theory of atoms in molecules (QTAIM) and non-covalent interactions (NCI) analysis. Ab initio mo-lecular dynamics (AIMD) calculations examined the temperature stability of the catalytic complex. Overall, Cu@Phosphorene appears to be an effective catalyst for converting CO₂ to formic acid and remains stable at higher temperatures, supporting efforts to mitigate climate change.

Abstract: To employ some simple physics-inspired density-based information-theoretic approach (ITA) quantities to appreciate the electron correlation energies is an unaccomplished and ongoing task. In this work, we expand the territory of the LR(ITA) (LR means linear regression) protocol to more complex systems, including (i) 24 octane isomers; (ii) polymeric structures, polyyne, polyene, all-trans-polymethineimine, and acene; (iii) molecular clusters, such as metallic Ben and Mgn, covalent Sn, hydrogen-bonded protonated water clusters H+(H2O)n, and dispersion-bound carbon dioxide (CO2)n, and benzene (C6H6)n clusters. With LR(ITA), one can simply predict the post-Hartree‒Fock (such as MP2 and coupled cluster) electron correlation energies at the cost of Hartree‒Fock calculations, even with chemical accuracy. For large molecular clusters, we employ the linear-scaling generalized energy-based fragmentation (GEBF) method to gauge the accuracy of LR(ITA). Employing benzene clusters as an illustration, the LR(ITA) method shows similar accuracy to that of GEBF. Overall, we have verified that ITA quantities can be used to predict the electron correlation energies of various complex systems.

Abstract: In this study, density functional theory (DFT) and time-dependent DFT (TD-DFT) methods were employed to investigate the geometrical, electronic, optical, and photovoltaic properties of graphene (G), silicon carbide (SiC), and graphene/hexagonal boron nitride (G/h-BN) nanostructures. Dimethyl sulfoxide (DMSO) was used as a solvent to enhance the electronic behavior of the systems. Key photovoltaic parameters such as open-circuit voltage (VOC), light harvesting efficiency (LHE), and the free energies of electron injection and regeneration were evaluated. Results revealed efficient electron injection from G, SiC, and G/h-BN into the conduction band of TiO₂. The HOMO levels of G and SiC were found above the I⁻/I₃⁻ redox potential, indicating potential limitations in electron regeneration; however, this was mitigated upon dissolution in DMSO. Additionally, DMSO improved optical absorption by red-shifting the UV–visible spectra. Overall, isolated G/h-BN and the DMSO-dissolved nanostructures show promise as sensitizers in dye-sensitized solar cells (DSSCs).

Abstract: The catalytic styrene Ph-CH=CH2 oxidation is assumed to be a simple reaction procedure, but its details require further systematic research. Using quantum-chemical treatment, relevant intermediates in various charge and spin states of three reaction pathways of styrene oxidation by hydroperoxyl were investigated. The reaction pathway A without any catalyst most probably proceeds by non-radical mechanism and 1[Ph-CH(O)-CH2OH]- is formed. The alternative formation of epoxide is energetically less advantageous. The reaction pathways B and C are based on the [CuL]- catalyst where H2L = trans-2,9-dibutyl-7,14-dimethyl-5,12-di(4-methoxyphenyl)-1,2,4,8,9,11-hexaazacyclotetradeca-7,14-diene-3,10-dione. Within reaction pathway B the neutral hydroperoxyl radical is bonded to Cu to form 2[CuL(OOH)]-. Subsequent addition of neutral styrene results in 2{[CuL(OH)](Ph-CH2-CHO)}- formation. The reaction pathway C starts with the initial non-radical formation of the π-complex 1[CuL(Ph-CH=CH2)]- which is problematic due to its endothermic character. Subsequent addition of a hydroperoxyl radical leads to 2{CuL[Ph-CH(OOH)-CH2]}-. Its oxidation leads to the separation of Ph-CH(OOH)-CH2.

Abstract: Using molecular dynamics simulations, we reveal how confinement in armchair MoS2 nanotubes alters the stability and melting points of hexagonal ice clusters. Ordered and hydrogen-disordered ice is studied inside and between nanotubes, showing a 30 K upward melting point shift for disordered interstitial ice due to hydrogen bond defects. The effects of nanotube diameter and ice impurities are quantified, highlighting MoS2’s potential in modulating phase transitions for applications in cryobiology and materials science.

Abstract: In this survey, we begin with a concise introduction to information theory within Shannon's framework, focusing on the key concept of Shannon entropy and its related quantities: relative entropy, joint entropy, conditional entropy and mutual information. We then demonstrate how to apply these information-theoretic tools in quantum chemistry, adopting either classical or quantum formalisms based on the choice of information carrier involved.

Abstract: Cancer has a threatening impact on human health, and it is one of the primary causes of fatalities worldwide. Different conventional treatments have been employed to treat cancer, but their non-specific nature reduces their therapeutic efficacy. This study employs a C5N2-based targeted drug carrier to study the delivery mechanism of anticancer drugs, particularly cisplatin, carmustine, and mechlorethamine, using density functional theory (DFT). The geometries of the drugs, the C5N2 substrate, and the drug@C5N2 complexes were optimized at the PBE0-D3BJ/def2SVP level of theory. Interaction energy has been computed for the complexes which follow the trend, i.e., cisplatin@C5N2 > carmustine@C5N2 > mechlorethamine@C5N2. The NCI and QTAIM analyses confirmed the presence of van der Waals forces between the carmustine@C5N2 and mechlorethamine@C5N2 complexes, while weak hydrogen bonding has also been observed between the cisplatin@C5N2 complex. ELF analysis has been performed to analyze the degree of delocalization of electrons within the complexes, which manifests consistency with the findings of NCI and QTAIM analyses. The electronic properties of the analytes and the C5N2 substrate have been examined through FMO, CRDs, DOS, NBO, and EDD analyses. FMO, CRDs, and DOS analysis confirmed the enhanced reactivity of the complexes. NBO illustrated an electron density shift between the drugs and the C5N2 sheet, while EDD exhibited a substantial correlation with the NBO findings. Recovery time has been determined to assess the biocompatibility and the desorption behavior of the drugs. Moreover, negative solvation energies and increased dipole moments in a solvent phase manifested enhanced solubility and easy circulation of the drugs in biological media. Subsequently, this study illustrates that cisplatin@C5N2, carmustine@C5N2, and mechlorethamine@C5N2 complexes can be utilized as efficient drug delivery systems.

Abstract: Silicon–lithium clusters are promising candidates for hydrogen storage due to their lightweight composition, high gravimetric capacities, and favorable non-covalent binding characteristics. In this study, we employ density functional theory (DFT), global optimization (AUTOMATON and Kick-MEP), and Born–Oppenheimer molecular dynamics (BOMD) simulations to evaluate the structural stability and hydrogen storage performance of key Li–Si systems. Potential energy surface (PES) exploration reveals that the true global minima of Li6Si6 and Li10Si10 differ markedly from previously proposed aromatic analogs based on benzene and naphthalene motifs. Instead, these clusters adopt compact geometries composed of one or two Si4 (Td) units and a Si2 dimer, all stabilized by surrounding Li atoms. Motivated by the recurrence of the Si4–Td motif—previously shown to exhibit three-dimensional σ-aromaticity—we explore oligomers of Li4Si4, confirming additive H2 uptake across dimer, trimer, and tetramer assemblies. Within the series of Si–Li clusters evaluated the Li12Si5 sandwich complex, featuring a σ-aromatic Si5 ring encapsulated by two Li6 units, achieves the highest hydrogen capacity, adsorbing 34 H2 molecules with a gravimetric density of 23.45 wt%. Its enhanced performance arises from the high density of accessible Li⁺ adsorption sites and the electronic stabilization afforded by delocalized σ-bonding. BOMD simulations at 300 and 400 K confirm dynamic stability and reversible storage behavior, while analysis of the interaction regions confirms that hydrogen adsorption proceeds via weak, dispersion-driven physisorption. These findings clarify structure-property relationships in Si–Li clusters and provide a basis for designing modular, lightweight, and thermally stable hydrogen storage materials.

Abstract: A reduced-order model (ROM) procedure for a one-dimensional steady plug-flow reactor (PFR) is developed and used to investigate the performance of a membrane reactor (MR), or membrane module (MM), for hydrogen separation from syngas that may be produced in an integrated gasification combined cycle (IGCC). A feed syngas enters from one side into a retentate zone, while a sweep gas of nitrogen enters from the opposite side into a neighbor permeate zone. The two zones are separated by permeable palladium membrane surfaces that are selectively permeable to hydrogen. After analyzing the hydrogen permeation profile in a base case (300 °C uniform temperature, 40 atm absolute retentate pressure, and 20 atm absolute permeate pressure), the temperature of the module, the retentate-side pressure, and the permeate-side pressure were varied individually and their influence on the permeation performance is investigated. In all the simulation cases, fixed targets of 95% hydrogen recovery and 40% mole-fraction of hydrogen at the permeate exit are demanded. The module length is allowed to change in order to satisfy these targets. Other dependent permeation-performance variables that are investigated include the logarithmic mean pressure-square-root difference, the hydrogen apparent permeance, and the efficiency factor of the hydrogen permeation. Various linear and nonlinear regression models are proposed based on the obtained results. This work gives general insights into hydrogen permeation via palladium membranes in a hydrogen membrane reactor (HMR). For example, the temperature is the most effective factor to improve the permeation performance. Increasing the absolute retentate pressure from the base value of 40 atm to 120 atm results in a proportional gain in the permeated hydrogen mass flux, with about 0.05 kg/m².hr gained per 1 atm increase in the retentate pressure; while decreasing the absolute permeate pressure from the base value of 20 bar to 0.2 bar causes the hydrogen mass flux to increase exponentially from 1.15 kg/m².hr. to 5.11 kg/m².hr.

Abstract: A combined crystallographic/computational analysis focused on the supramolecular features of the crystal structure of N,N'-diethyloxamide (NNDO) is discussed in this work. The studied compound was obtained unexpectedly during the synthesis of a series of salts of cyclic oximes derivatives. In the solid state NNDO is stabilized essentially through a strong N–H···O hydrogen bond but Hirshfeld surface analysis and Density Functional Theory (DFT) calculations were carried out to evaluate the strength of the predominant hydrogen bonds observed in the X-ray structure, as well as the secondary CH···O and CH···N contacts established between the ethyl groups and the perpendicular dioxamide group. These interactions were further investigated using a combination of Quantum Theory of Atoms in Molecules (QTAIM) and Non-Covalent Interaction Plot (NCIplot) computational tools, and were rationalized using Molecular Electrostatic Potential (MEP) surface calculations.