Search

The transformative impact of modern computational paradigms and technologies, such as high-performance computing (HPC), quantum computing, and cloud computing, has opened up profound new opportunities for scientific simulations. Scalable computational chemistry is one beneficiary of this technological progress. The main focus of this paper is on the performance of various quantum chemical formulations, ranging from low-order methods to high-accuracy approaches, implemented in different computational chemistry packages and libraries, such as NWChem, NWChemEx, Scalable Predictive Methods for Excitations and Correlated Phenomena, ExaChem, and Fermi–Löwdin orbital self-interaction correction on Azure Quantum Elements, Microsoft's cloud services platform for scientific discovery. We pay particular attention to the intricate workflows for performing complex chemistry simulations, associated data curation, and mechanisms for accuracy assessment, which is demonstrated with the Arrows automated workflow for high throughput simulations. Finally, we provide a perspective on the role of cloud computing in supporting the mission of leadership computational facilities. [ABSTRACT FROM AUTHOR]

Point Group (PG) symmetries play a fundamental role in many aspects of theoretical chemistry and computational materials science. With the objective to automatize the search of PG symmetry operations of generic atomic clusters, we present a new algorithm called Symmetry Operation FInder (SOFI). SOFI addresses the problem of identifying PG symmetry by framing it as a degenerate shape-matching problem, where the multiple solutions correspond to distinct symmetry operations. The developed algorithm is compared against three other algorithms dedicated to PG identification on a large set of atomic clusters. The results, along with some illustrative use cases, showcase the effectiveness of SOFI. The SOFI algorithm is released as part of the iterative rotations and assignments library, accessible at https://github.com/mammasmias/IterativeRotationsAssignments. [ABSTRACT FROM AUTHOR]

Western blot is a popular biomolecular analysis method for measuring the relative quantities of independent proteins in complex biological samples. However, variability in quantitative western blot data analysis poses a challenge in designing reproducible experiments. The lack of rigorous quantitative approaches in current western blot statistical methodology may result in irreproducible inferences. Here we describe best practices for the design and analysis of western blot experiments, with examples and demonstrations of how different analytical approaches can lead to widely varying outcomes. To facilitate best practices, we have developed the blotRig tool for designing and analyzing western blot experiments to improve their rigor and reproducibility. The blotRig application includes functions for counterbalancing experimental design by lane position, batch management across gels, and analytics with covariates and random effects.

The development and benchmarking of computational chemistry methods rely on comparison with benchmark data. More and larger benchmark datasets are becoming available, and working efficiently with them is a necessity. The Cuby framework provides rich functionality for working with datasets, comes with many ready-to-use predefined benchmark sets, and interfaces with a wide range of computational chemistry software packages. Here, we review the tools Cuby provides for working with datasets and provide examples of more advanced workflows, such as handling large numbers of computations on high performance computing resources and reusing previously computed data. Cuby has also been extended recently to include two important benchmark databases, NCIAtlas and GMTKN55. [ABSTRACT FROM AUTHOR]

The gas-phase rotational spectrum from 8 to 750 GHz and the high-resolution infrared (IR) spectrum of pyridazine (o-C4H4N2) have been analyzed for the ground and four lowest-energy vibrationally excited states. A combined global fit of the rotational and IR data has been obtained using a sextic, centrifugally distorted-rotor Hamiltonian with Coriolis coupling between appropriate states. Coriolis coupling has been addressed in the two lowest-energy coupled dyads (ν16, ν13 and ν24, ν9). Utilizing the Coriolis coupling between the vibrational states of each dyad and the analysis of the IR spectrum for ν16 and ν9, we have determined precise band origins for each of these fundamental states: ν16 (B1) = 361.213 292 7 (17) cm−1, ν13 (A2) = 361.284 082 4 (17) cm−1, ν24 (B2) = 618.969 096 (26) cm−1, and ν9 (A1) = 664.723 378 4 (27) cm−1. Notably, the energy separation in the ν16-ν13 Coriolis-coupled dyad is one of the smallest spectroscopically measured energy separations between vibrational states: 2122.222 (72) MHz or 0.070 789 7 (24) cm−1. Despite ν13 being IR inactive and ν24 having an impractically low-intensity IR intensity, the band origins of all four vibrational states were measured, showcasing the power of combining the data provided by millimeter-wave and high-resolution IR spectra. Additionally, the spectra of pyridazine-dx isotopologues generated for a previous semi-experimental equilibrium structure (reSE) determination allowed us to analyze the two lowest-energy vibrational states of pyridazine for all nine pyridazine-dx isotopologues. Coriolis-coupling terms have been measured for analogous vibrational states across seven isotopologues, both enabling their comparison and providing a new benchmark for computational chemistry. [ABSTRACT FROM AUTHOR]

Hybrid quantum mechanics/molecular mechanics (QM/MM) simulations have become an essential tool in computational chemistry, particularly for analyzing complex biological and condensed phase systems. Building on this foundation, our work presents a novel implementation of the Gaussian Electrostatic Model (GEM), a polarizable density-based force field, within the QM/MM framework. This advancement provides seamless integration, enabling efficient and optimized QM/GEM calculations in a single step using the LICHEM Code. We have successfully applied our implementation to water dimers and hexamers, demonstrating the ability to handle water systems with varying numbers of water molecules. Moreover, we have extended the application to describe the double proton transfer of the aspartic acid dimer in a box of water, which highlights the method's proficiency in investigating heterogeneous systems. Our implementation offers the flexibility to perform on-the-fly density fitting or to utilize pre-fitted coefficients to estimate exchange and Coulomb contributions. This flexibility enhances efficiency and accuracy in modeling molecular interactions, especially in systems where polarization effects are significant. [ABSTRACT FROM AUTHOR]

Grid is a free and open-source Python library for constructing numerical grids to integrate, interpolate, and differentiate functions (e.g., molecular properties), with a strong emphasis on facilitating these operations in computational chemistry and conceptual density functional theory. Although designed, maintained, and released as a stand-alone Python library, Grid was originally developed for molecular integration, interpolation, and solving the Poisson equation in the HORTON and ChemTools packages. Grid is designed to be easy to use, extend, and maintain; this is why we use Python and adopt many principles of modern software development, including comprehensive documentation, extensive testing, continuous integration/delivery protocols, and package management. We leverage popular scientific packages, such as NumPy and SciPy, to ensure high efficiency and optimized performance in grid development. This article is the official release note of the Grid library showcasing its unique functionality and scope. [ABSTRACT FROM AUTHOR]

This research examines the nonadiabatic dynamics of cyclobutanone after excitation into the n → 3s Rydberg S2 state. It stems from our contribution to the Special Topic of the Journal of Chemical Physics to test the predictive capability of computational chemistry against unseen experimental data. Decoherence-corrected fewest-switches surface hopping was used to simulate nonadiabatic dynamics with full and approximated nonadiabatic couplings. Several simulation sets were computed with different electronic structure methods, including a multiconfigurational wavefunction [multiconfigurational self-consistent field (MCSCF)] specially built to describe dissociative channels, multireference semiempirical approach, time-dependent density functional theory, algebraic diagrammatic construction, and coupled cluster. MCSCF dynamics predicts a slow deactivation of the S2 state (10 ps), followed by an ultrafast population transfer from S1 to S0 (<100 fs). CO elimination (C3 channel) dominates over C2H4 formation (C2 channel). These findings radically differ from the other methods, which predicted S2 lifetimes 10–250 times shorter and C2 channel predominance. These results suggest that routine electronic structure methods may hold low predictive power for the outcome of nonadiabatic dynamics. [ABSTRACT FROM AUTHOR]

Electronic structure theory, i.e., quantum chemistry, is the fundamental building block for many problems in computational chemistry. We present a new distributed computing framework (BigChem), which allows for an efficient solution of many quantum chemistry problems in parallel. BigChem is designed to be easily composable and leverages industry-standard middleware (e.g., Celery, RabbitMQ, and Redis) for distributed approaches to large scale problems. BigChem can harness any collection of worker nodes, including ones on cloud providers (such as AWS or Azure), local clusters, or supercomputer centers (and any mixture of these). BigChem builds upon MolSSI packages, such as QCEngine to standardize the operation of numerous computational chemistry programs, demonstrated here with Psi4, xtb, geomeTRIC, and TeraChem. BigChem delivers full utilization of compute resources at scale, offers a programable canvas for designing sophisticated quantum chemistry workflows, and is fault tolerant to node failures and network disruptions. We demonstrate linear scalability of BigChem running computational chemistry workloads on up to 125 GPUs. Finally, we present ChemCloud, a web API to BigChem and successor to TeraChem Cloud. ChemCloud delivers scalable and secure access to BigChem over the Internet. [ABSTRACT FROM AUTHOR]

The observation that materials can change their properties when placed inside or near an optical resonator has sparked a fervid interest in understanding the effects of strong light–matter coupling on molecular dynamics, and several approaches have been proposed to extend the methods of computational chemistry into this regime. Whereas the majority of these approaches have focused on modeling a single molecule coupled to a single cavity mode, changes to chemistry have so far only been observed experimentally when very many molecules are coupled collectively to multiple modes with short lifetimes. While atomistic simulations of many molecules coupled to multiple cavity modes have been performed with semi-classical molecular dynamics, an explicit description of cavity losses has so far been restricted to simulations in which only a very few molecular degrees of freedom were considered. Here, we have implemented an effective non-Hermitian Hamiltonian to explicitly treat cavity losses in large-scale semi-classical molecular dynamics simulations of organic polaritons and used it to perform both mean-field and surface hopping simulations of polariton relaxation, propagation, and energy transfer. [ABSTRACT FROM AUTHOR]

SHP2, a pivotal component downstream of both receptor and non-receptor tyrosine kinases, has been underscored in the progression of various human cancers and neurodevelopmental disorders. Allosteric inhibitors have been proposed to regulate its autoinhibition. However, oncogenic mutations, such as E76K, convert SHP2 into its open state, wherein the catalytic cleft becomes fully exposed to its ligands. This study elucidates the dynamic properties of SHP2 structures across different states, with a focus on the effects of oncogenic mutation on two known binding sites of allosteric inhibitors. Through extensive modeling and simulations, we further identified an alternative allosteric binding pocket in solution structures. Additional analysis provides insights into the dynamics and stability of the potential site. In addition, multi-tier screening was deployed to identify potential binders targeting the potential site. Our efforts to identify a new allosteric site contribute to community-wide initiatives developing therapies using multiple allosteric inhibitors to target distinct pockets on SHP2, in the hope of potentially inhibiting or slowing tumor growth associated with SHP2.

Machine learning potentials (MLPs) have attracted significant attention in computational chemistry and materials science due to their high accuracy and computational efficiency. The proper selection of atomic structures is crucial for developing reliable MLPs. Insufficient or redundant atomic structures can impede the training process and potentially result in a poor quality MLP. Here, we propose a local-environment-guided screening algorithm for efficient dataset selection in MLP development. The algorithm utilizes a local environment bank to store unique local environments of atoms. The dissimilarity between a particular local environment and those stored in the bank is evaluated using the Euclidean distance. A new structure is selected only if its local environment is significantly different from those already present in the bank. Consequently, the bank is then updated with all the new local environments found in the selected structure. To demonstrate the effectiveness of our algorithm, we applied it to select structures for a Ge system and a Pd13H2 particle system. The algorithm reduced the training data size by around 80% for both without compromising the performance of the MLP models. We verified that the results were independent of the selection and ordering of the initial structures. We also compared the performance of our method with the farthest point sampling algorithm, and the results show that our algorithm is superior in both robustness and computational efficiency. Furthermore, the generated local environment bank can be continuously updated and can potentially serve as a growing database of feature local environments, aiding in efficient dataset maintenance for constructing accurate MLPs. [ABSTRACT FROM AUTHOR]

Density functional theory (DFT) stands as a cornerstone method in computational quantum chemistry and materials science due to its remarkable versatility and scalability. Yet, it suffers from limitations in accuracy, particularly when dealing with strongly correlated systems. To address these shortcomings, recent work has begun to explore how machine learning can expand the capabilities of DFT: an endeavor with many open questions and technical challenges. In this work, we present GradDFT a fully differentiable JAX-based DFT library, enabling quick prototyping and experimentation with machine learning-enhanced exchange–correlation energy functionals. GradDFT employs a pioneering parametrization of exchange–correlation functionals constructed using a weighted sum of energy densities, where the weights are determined using neural networks. Moreover, GradDFT encompasses a comprehensive suite of auxiliary functions, notably featuring a just-in-time compilable and fully differentiable self-consistent iterative procedure. To support training and benchmarking efforts, we additionally compile a curated dataset of experimental dissociation energies of dimers, half of which contain transition metal atoms characterized by strong electronic correlations. The software library is tested against experimental results to study the generalization capabilities of a neural functional across potential energy surfaces and atomic species, as well as the effect of training data noise on the resulting model accuracy. [ABSTRACT FROM AUTHOR]

A major limitation of amine-based post-combustion carbon capture technology is the necessity to regenerate amines at high temperatures, which dramatically increases operating costs. This paper concludes the effect of solvent choice as a possible route to modify the thermodynamics and kinetics characterizing the involved amine regeneration reactions and discusses whether these modifications can be economically beneficial. We report experimentally benchmarked computational chemistry calculations of monoethanolamine regeneration reactions employing aqueous and non-aqueous solvents with a wide range of dielectric constants. Unlike previous studies, our improved computational chemistry framework could accurately reproduce the right experimental activation energy of zwitterion formation. From the thermodynamics and kinetics of the predicted reactions, the use of non-aqueous solvents with small dielectric constants led to reductions in regeneration Gibbs free energies, activation barriers, and enthalpy changes. This can reduce energy consumption and give an opportunity to run desorption columns at relatively lower temperatures, thus offering the possibility of relying on low-grade waste heat as an energy input. [ABSTRACT FROM AUTHOR]

We have investigated, using molecular dynamics, the surface chemistry of hydrogen incident on the amorphous and crystalline lithium oxide and lithium hydroxide surfaces upon being slowed down by a collision cascade and retained in the amorphous surface of either Li2O or LiOH. We looked for the bonding of H to the resident structures in the surface to understand a possible chain of chemical reactions that can lead to surface transformation upon H atom impact. Our findings, using Density-Functional Theory (DFT) trained ReaxFF force field/electronegativity equalization method potentials, stress the importance of inclusion of polarization in the dynamics of a Li–O–H system, which is also illustrated by DFT energy minimization and quantum–classical molecular dynamics using tight binding DFT. The resulting polar-covalent chemistry of the studied systems is complex and very sensitive to the instantaneous positions of all atoms as well as the ratio of concentrations of various resident atoms in the surface. [ABSTRACT FROM AUTHOR]

The traditional foundation of science lies on the cornerstones of theory and experiment. Theory is used to explain experiment, which in turn guides the development of theory. Since the advent of computers and the development of computational algorithms, computation has risen as the third cornerstone of science, joining theory and experiment on an equal footing. Computation has become an essential part of modern science, amending experiment by enabling accurate comparison of complicated theories to sophisticated experiments, as well as guiding by triage both the design and targets of experiments and the development of novel theories and computational methods. Like experiment, computation relies on continued investment in infrastructure: it requires both hardware (the physical computer on which the calculation is run) as well as software (the source code of the programs that performs the wanted simulations). In this Perspective, I discuss present-day challenges on the software side in computational chemistry, which arise from the fast-paced development of algorithms, programming models, as well as hardware. I argue that many of these challenges could be solved with reusable open source libraries, which are a public good, enhance the reproducibility of science, and accelerate the development and availability of state-of-the-art methods and improved software. [ABSTRACT FROM AUTHOR]

The solvent effects on the linear and second-order nonlinear optical properties of an aminonaphtylethenylpyridinium (ANEP) dye are investigated by combining experimental and theoretical chemistry methods. On the one hand, deep near infrared (NIR) hyper-Rayleigh scattering (HRS) measurements (1840–1950 nm) are performed on solutions of di-8-ANEPPS in deuterated chloroform, dimethylformamide, and dimethylsulfoxide to determine their first hyperpolarizablity (βHRS). For the first time, these HRS experiments are carried out in the picosecond regime in the deep NIR with very moderate (≤3 mW) average input power, providing a good signal-to-noise ratio and avoiding solvent thermal effects. Moreover, the frequency dispersion of βHRS is investigated for Disperse Red 1 (DR1), a dye commonly used as HRS external reference. On the other hand, these are compared with computational chemistry results obtained by using a sequential molecular dynamics (MD) then quantum mechanics (QM) approach. The MD method allows accounting for the dynamical nature of the molecular structures. Then, the QM part is based on TDDFT/M06-2X/6-311+G* calculations using solvation models ranging from continuum to discrete ones. Measurements report a decrease of the βHRS of di-8-ANEPPS in more polar solvents and these effects are reproduced by the different solvation models. For di-8-ANEPPS and DR1, comparisons show that the use of a hybrid solvation model, combining the description of the solvent molecules around the probe by point charges with a continuum model, already achieves quasi quantitative agreement with experiment. These results are further improved by using a polarizable embedding that includes the atomic polarizabilities in the solvent description. [ABSTRACT FROM AUTHOR]

Catalyzed by enormous success in the industrial sector, many research programs have been exploring data-driven, machine learning approaches. Performance can be poor when the model is extrapolated to new regions of chemical space, e.g., new bonding types, new many-body interactions. Another important limitation is the spatial locality assumption in model architecture, and this limitation cannot be overcome with larger or more diverse datasets. The outlined challenges are primarily associated with the lack of electronic structure information in surrogate models such as interatomic potentials. Given the fast development of machine learning and computational chemistry methods, we expect some limitations of surrogate models to be addressed in the near future; nevertheless spatial locality assumption will likely remain a limiting factor for their transferability. Here, we suggest focusing on an equally important effort—design of physics-informed models that leverage the domain knowledge and employ machine learning only as a corrective tool. In the context of material science, we will focus on semi-empirical quantum mechanics, using machine learning to predict corrections to the reduced-order Hamiltonian model parameters. The resulting models are broadly applicable, retain the speed of semiempirical chemistry, and frequently achieve accuracy on par with much more expensive ab initio calculations. These early results indicate that future work, in which machine learning and quantum chemistry methods are developed jointly, may provide the best of all worlds for chemistry applications that demand both high accuracy and high numerical efficiency. [ABSTRACT FROM AUTHOR]

Computing accurate yet efficient approximations to the solutions of the electronic Schrödinger equation has been a paramount challenge of computational chemistry for decades. Quantum Monte Carlo methods are a promising avenue of development as their core algorithm exhibits a number of favorable properties: it is highly parallel and scales favorably with the considered system size, with an accuracy that is limited only by the choice of the wave function Ansatz. The recently introduced machine-learned parametrizations of quantum Monte Carlo Ansätze rely on the efficiency of neural networks as universal function approximators to achieve state of the art accuracy on a variety of molecular systems. With interest in the field growing rapidly, there is a clear need for easy to use, modular, and extendable software libraries facilitating the development and adoption of this new class of methods. In this contribution, the DeepQMC program package is introduced, in an attempt to provide a common framework for future investigations by unifying many of the currently available deep-learning quantum Monte Carlo architectures. Furthermore, the manuscript provides a brief introduction to the methodology of variational quantum Monte Carlo in real space, highlights some technical challenges of optimizing neural network wave functions, and presents example black-box applications of the program package. We thereby intend to make this novel field accessible to a broader class of practitioners from both the quantum chemistry and the machine learning communities. [ABSTRACT FROM AUTHOR]

Background: Target identification and hit identification can be transformed through the application of biomedical knowledge analysis, AI-driven virtual screening and robotic cloud lab systems. However there are few prospective studies that evaluate the efficacy of such integrated approaches. Results: We synergistically integrate our in-house-developed target evaluation (SpectraView) and deep-learning-driven virtual screening (HydraScreen) tools with an automated robotic cloud lab designed explicitly for ultra-high-throughput screening, enabling us to validate these platforms experimentally. By employing our target evaluation tool to select IRAK1 as the focal point of our investigation, we prospectively validate our structure-based deep learning model. We can identify 23.8% of all IRAK1 hits within the top 1% of ranked compounds. The model outperforms traditional virtual screening techniques and offers advanced features such as ligand pose confidence scoring. Simultaneously, we identify three potent (nanomolar) scaffolds from our compound library, 2 of which represent novel candidates for IRAK1 and hold promise for future development. Conclusion: This study provides compelling evidence for SpectraView and HydraScreen to provide a significant acceleration in the processes of target identification and hit discovery. By leveraging Ro5's HydraScreen and Strateos' automated labs in hit identification for IRAK1, we show how AI-driven virtual screening with HydraScreen could offer high hit discovery rates and reduce experimental costs. Scientific contribution: We present an innovative platform that leverages Knowledge graph-based biomedical data analytics and AI-driven virtual screening integrated with robotic cloud labs. Through an unbiased, prospective evaluation we show the reliability and robustness of HydraScreen in virtual and high-throughput screening for hit identification in IRAK1. Our platforms and innovative tools can expedite the early stages of drug discovery. [ABSTRACT FROM AUTHOR]

The minimization of the commutator of the Fock and density matrices as the error matrix in the direct inversion of the iterative subspace (CDIIS) developed by Pulay is a powerful self‐consistent field (SCF) acceleration technique for the construction of optimum Fock matrix, if initiated with a fair initial guess. In this work, we present an alternative minimized error matrix to the commutator in the CDIIS, namely the residual or the gradient of the energy‐functional for a Slater determinant subject to the orthonormality constraints among orbitals, representing the search for a newly improved Fock matrix in the direction of the residual in the direct inversion of the iterative subspace (RDIIS). Implemented in the computational chemistry package GAMESS, the RDIIS is compared with the standard CDIIS and the second order SCF orbital optimization (SOSCF) for tested molecules started with a crude guess. As a result, the RDIIS stably and efficiently performs the SCF convergence acceleration. Furthermore, the RDIIS is considerably independent on the subspace size with the concentrated linear coefficients accounting proportionally for the Fock matrices close to the current iteration. [ABSTRACT FROM AUTHOR]

Structural diversity of diterpenes is mediated by the enigmatic family of diterpene synthases. The overall enzymatic contribution hereby lies in a carefully concerted chemistry of highly reactive carbocation intermediates mainly guided by aromatic and polar amino acid side chains and the pyrophosphate cofactor. To date several studies aimed to shed light on the mechanism underlining terpene synthases chemistry. Specifically, the diterpene synthase CotB2 serves as model enzyme for detailed mutagenesis studies. Here we investigate the catalytic mechanism of CotB2 variant V80L in a holistic, biochemical, structural, and computational biology approach. We were able to identify an altered product profile compared to CotB2WT for the substrates geranylgeranyl diphosphate and farnesyl diphosphate. Moreover, we solved the crystal structure, and shed further light on terpene synthase chemistry by modelling of the substrate and intermediate binding. [ABSTRACT FROM AUTHOR]

Conformational ensemble generation and the search for the global minimum conformation are important problems in computational chemistry. In this work, a variant on the conformer‐rotamer ensemble sampling tool (CREST) iterative metadynamics (iMTD) algorithm designed for determining structural ensembles and energetics of noncovalent clusters of flexible molecules is presented. We term this new algorithm a low‐energy diversity‐enhanced variant on CREST, or LEDE‐CREST. As with CREST, the energies are evaluated using the semiempirical GFN2‐xTB extended tight binding approach. The utility of the algorithm is highlighted by generating ensembles for a variety of noncovalent clusters of flexible or rigid monomers using both CREST and LEDE‐CREST. [ABSTRACT FROM AUTHOR]

In coordination chemistry, 2,2′:6′,2″-terpyridine is a versatile and extensively studied tridentate ligand. Terpyridine forms stable complexes with a variety of metal ions through coordination sites provided by the three nitrogen atoms in its pyridine rings. This paper presents an electrochemical study on various bis(terpyridine)osmium(II) complexes, addressing the absence of a systematic investigation into their redox behavior. Additionally, a computational chemistry analysis was conducted on these complexes, as well as on eight previously studied osmium(II)-bipyridine and -phenanthroline complexes, to expand both the experimental and theoretical understanding. The experimental redox potentials, Hammett constants, and DFT-calculated energies show linear correlations due to the electron-donating or electron-withdrawing nature of the substituents, as described by the Hammett constants. These substituent effects cause shifts to lower or higher redox potentials, respectively. [ABSTRACT FROM AUTHOR]

(E)-1-benzyl-3-(2-pyridine-2-yl)hydrazono)indolin-2-one (BPHI), an aromatic moiety, were subjected to a collection of theoretical studies with the help of the Gaussian 09, GaussView 6.0, and Multiwfn-3.8 packages of software. Its molecular geometry, bond angle, and bond length are computed theoretically. Vibrational assignments and PED values are calculated theoretically and were corroborated with experimental IR spectra to gain insight and knowledge about the structural details of BPHI. The theoretical determination of the electronic transitions along with photophysical properties for various mediums (viz., gas, acetonitrile, DMSO), including HOMO and LUMO energy gap are theoretically found from time-dependent—density functional theory (TD-DFT). Interpretation of the Fukui function helps in identifying the active sites in BPHI. To study the topology of BPHI, ELF maps (electron localization function) have been utilized. Alongside, detailed analyses of RDG (reduced density gradient) as well as LOL (localized orbital locator) are also carried out. Interactions pertaining to donor and acceptor moiety in BPHI are computed through natural bond analysis. MEP analysis is used to determine the bioactive site of BPHI. 4F5S crystal structure with respect to BSA protein was used for molecular docking with the help of the CB-dock software and docked results are visualized by Molegro molecular view software. [ABSTRACT FROM AUTHOR]

Poly (ethylene glycol dimethacrylate-1,2-ethanedithiol) (P(EDGMA-EDT)) and poly (ethylene glycol dimethacrylate-3,6-dioxy-1,8-octanedithiol) (P(EDGMA-DODT)) are excellent solid polymer electrolytes synthesized experimentally. The coordination structure, redox properties, and modification design of P(EDGMA-EDT)-LiTFSI and P(EDGMA-DODT)-LiTFSI are investigated by density functional theory. The theoretical simulation of infrared spectra and the coordination structure information show that the ester carbonyl group in P(EDGMA-EDT) and the ester carbonyl group and ether oxygen in P(EDGMA-DODT) interact with Li+ ions. Li+ ion coordination numbers in these two electrolytes are 4 and 5; thioether and ester groups in polymers are their redox active sites, respectively. Modified sulfone oxide P(EDGMA-EDT)2-SO2 and P(EDGMA-DODT)2-SO2 cannot only maintain the reduction stability but also greatly improve the oxidation potential. LiPF6, LiDFBOP, and LiBF3Cl are good candidates for sulfone-based polymers. The electron-withdrawing groups (− 4Br, − 4Cl, − 4F, − NO2, and − CN) substitution can improve the oxidation potential of P(EDGMA-EDT) but have little effect on the oxidation potential of P(EDGMA-DODT) and reduce the reduction stability of both polymers. These findings provide theoretical guidance for the coordination structure as well as the molecular design of solid electrolytes containing ester groups and thioether. [ABSTRACT FROM AUTHOR]

In this study, new Cu (II) and Zn (II) chelates (3–6) of 3‐/or 4‐ethyl and azo chromophore groups (NN) containing Schiff base ligands (2‐{(E)‐[(4‐ethylphenyl)imino]methyl}‐4‐[(E)‐phenyldiazenyl]phenol, HL1 (1) and 2‐{(E)‐[(3‐ethylphenyl)imino]methyl}‐4‐[(E)‐phenyldiazenyl]phenol, HL2 (2) were prepared and characterized by the analytical and spectroscopic methods. Crystal structures of chelates 3, 4 and 6 were determined by single‐crystal X‐ray diffraction studies. The ligands behaved as bidentates, coordinating through the nitrogen atom of the azomethine group (CHN) and the oxygen atom of the α‐hydroxyl group (OH). The metal:ligand ratio in the prepared copper (II) and zinc (II) chelates was found to be 1:2 by analytical and spectral analysis. The bands in the infrared spectra of the metal chelates in the region of 522–504 cm−1 and 467–455 cm−1 are attributed to ν(MO) and ν(MN) vibrations, respectively. When the DNA binding activities of the compounds were examined, it was determined that compound 4 had the highest binding constant. Fluorescence and viscosity data showed that the compounds interact with DNA through minor grove binding mode. Furthermore, anticancer properties of the compounds were investigated. Ligand 2 was found to be highly effective against the HepG2 cell with an IC50 of 0.31 μM. In addition, structural analysis, frontier molecular orbitals, molecular electrostatic potential maps, Hirshfeld surface analysis of the synthesized compounds using computational chemistry methods and anticancer activity studies were carried out by molecular docking. [ABSTRACT FROM AUTHOR]

Because conventional inhibitors are nonbiodegradable and harmful, the development of ecofriendly corrosion inhibitors is gaining popularity. The anti-corrosive efficacy of 2-(2-Methyl-5-nitro-1H-imidazol-1-yl) ethanol on carbon steel in an chloride solution was evaluated in this study using a variety of approaches such as electrochemical measurements and computational studies. The results showed that increasing the concentration of 2-(2-Methyl-5-nitro-1H-imidazol-1-yl) ethanol from 0.1 to 3.0 g/L enhanced the inhibition efficiency (IE%) to 90.18-91.71 %. The high ΔE(eV) value of 0.239 and the interaction and binding energies of - 995.45 and 995.45 for 2-(2-Methyl-5-nitro-1H-imidazol-1-yl) ethanol molecules onto Fe (110) substrate were further supported by quantum chemical analytics, which also supported the empirical results. The results show that 2-(2-Methyl-5-nitro-1H-imidazol-1-yl) ethanol has a bright future as an effective and environmentally safe inhibitor for preventing carbon steel from corroding in corrosive media. [ABSTRACT FROM AUTHOR]

Nanoporous carbon-based materials are being investigated as potential electrical double-layer-based ultracapacitors. The electrochemical properties of nanoporous carbon materials strongly depend upon their structure. Carbide-derived carbon (CDC) materials are considered promising carbon-based energy storage because of their diverse structural variations with high content of micropores, surface area, and pore size distribution. Considering all these facts, therefore, for the first time, a multi-target quantitative nanostructure property relationship (Mt-QnSPR) approach was used on a set of carbide-derived carbon materials to predict the electrical double-layer capacitance in non-aqueous electrolyte. Here, the volumetric capacitance of these carbon electrodes was predicted in terms of cathodic capacitance (CvNEG) and anodic capacitance (CvPOS) values using a single multi-target ANN model. Two models, one with experimentally derived structure descriptors and the other with descriptors derived from the Monte Carlo method, were developed and tested using an external test set. The prediction abilities of both models were compared using various statistical parameters. The results showed that both models were quite robust and reliable (R2test = 0.962, ΔRm2 = 0.038. CCC = 0.977, IIC = 0.872, RMSE = 2.610 for 5–5-2 ANN model; R2test = 0.858, ΔRm2 = 0.044, CCC = 0.925, IIC = 0.708, RMSE = 4.77 for 2–2-2 ANN model). But 5–5–2 model outperforms 2–2–2 model in terms of training and prediction ability. The applicability domain of these models was also verified using the leverage approach. [ABSTRACT FROM AUTHOR]

Aqueous organic redox flow batteries (AORFBs) have pioneered new routes for large-scale energy storage. The tunable nature of redox-active organic molecules provides a robust foundation for creating innovative AORFBs with exceptional performance. Molecular engineering endows various organic molecules with considerable advantages in solubility, stability, and redox potential. Advanced characterizations have enabled a comprehensive understanding of the redox reaction and degradation mechanisms of these organic molecules. Computational chemistry and machine learning have guided the development of new organic molecules. The practical application of AORFBs will depend on the complementary efforts of multiple parties. This paper consolidates the current design principles of molecular engineering, degradation mechanisms, characterization techniques, and the utilization of computational chemistry. It also offers perspectives and forecasts the necessary attributes and strategic efforts for the next-generation AORFBs, aiming to provide the research community with a deeper understanding. [ABSTRACT FROM AUTHOR]

A topological index is a numerical quantity for the structure graph of the molecule and it can be represented through graph theory. Also, its application not only in the field of chemistry can also be applied in areas including computer science, networking, etc. Hence, this paper introduces the Randic index of a vague graph and vague subgraph with their properties. The upper and lower boundaries of the Randic index of vague graphs are studied with some isomorphic properties. Likewise, the Randic index of directed vague graphs is introduced. Finally, an application of the Randic index in construction has been presented. [ABSTRACT FROM AUTHOR]

Chemically initiated oxidative polymerization stands out as the most suitable method for the large‐scale and controllable synthesis of perfluoropolyether (PFPE). However, the mechanism and related reaction kinetics of this synthesis reaction remain elusive. In this study, PFPE was synthesized through the copolymerization of hexafluoropropylene and oxygen, initiated by fluorine. Subsequently, the synthesis mechanism of this chemically initiated oxidative polymerization was first explored using density functional theory. Simulation results yielded a comprehensive reaction network of the synthesis process, including chain initiation, propagation, decomposition, transfer, and termination. Meanwhile, a detailed kinetic model was constructed based on theoretical reaction rates of relevant elementary reactions. The effects of reaction operating conditions on the molecular weight of PFPE were experimentally investigated, with results in good agreement with the kinetic model. This work stablishes a solid foundation for optimizing and controlling the PFPE synthesis process. [ABSTRACT FROM AUTHOR]

Atomistic modeling can provide valuable insights into the design of novel heterogeneous catalysts as needed nowadays in the areas of, e. g., chemistry, materials science, and biology. Classical force fields and ab initio calculations have been widely adopted in molecular simulations. However, these methods usually suffer from the drawbacks of either low accuracy or high cost. Recently, the development of machine learning interatomic potentials (MLIPs) has become more and more popular as they can tackle the problems in question and can deliver rather accurate results at significantly lower computational cost. In this review, the atomistic modeling of catalytic systems with the aid of MLIPs is discussed, showcasing recently developed MLIP models and selected applications for the modeling of heterogeneous catalytic systems. We also highlight the best practices and challenges for MLIPs and give an outlook for future works on MLIPs in the field of heterogeneous catalysis. [ABSTRACT FROM AUTHOR]

The 57th Bürgenstock Conference on Organic Chemistry, held in Switzerland, featured over a hundred scientists from around the world engaging in vibrant scientific discussions. The conference covered a wide range of topics, including non-enzymatic RNA replication, carbon dioxide conversion, organic molecular cages, protein catalyst engineering, and (photo) redox catalysis. Presentations highlighted innovative research in various fields, such as peptide chemistry, metal catalysis, flow processing, and late-stage functionalization, providing valuable insights for the scientific community. [Extracted from the article]

The development of infrared difference spectroscopy provides unprecedented insights on structures of complex molecules like metalloproteins. However, the relevant information can be hard to find among the many bands of the vibrational spectra. The ab initio modeling is very helpful to assign the frequencies to vibrational modes but it is a challenge to process the huge quantity of data into descriptors useful for experimentalists. To this end, we developed a new tool called VIBMOL allowing to analyze vibrational modes of molecules from hessian matrices calculated with common quantum chemistry codes. VIBMOL program runs on Unix machines. Through a new graphical interface, the users can calculate the normal modes of molecules, visualize them, simulate infrared spectra, and explore the Potential Energy Distribution of normal modes among any set of vibration coordinates. It is combined with an interface program (gosdmu) formatting relevant data from the GAUSSIAN program. VIBMOL code is available upon request to the authors. A discussion is provided to help the readers to choose between a large choice of different software and it shows how VIBMOL can make the IR assignment easier in the context of collaborations with experimentalists. [ABSTRACT FROM AUTHOR]

Machine-learned computational chemistry has led to a paradoxical situation in which molecular properties can be accurately predicted, but they are difficult to interpret. Explainable AI (XAI) tools can be used to analyze complex models, but they are highly dependent on the AI technique and the origin of the reference data. Alternatively, interpretable real-space tools can be employed directly, but they are often expensive to compute. To address this dilemma between explainability and accuracy, we developed SchNet4AIM, a SchNet-based architecture capable of dealing with local one-body (atomic) and two-body (interatomic) descriptors. The performance of SchNet4AIM is tested by predicting a wide collection of real-space quantities ranging from atomic charges and delocalization indices to pairwise interaction energies. The accuracy and speed of SchNet4AIM breaks the bottleneck that has prevented the use of real-space chemical descriptors in complex systems. We show that the group delocalization indices, arising from our physically rigorous atomistic predictions, provide reliable indicators of supramolecular binding events, thus contributing to the development of Explainable Chemical Artificial Intelligence (XCAI) models. Chemical AI often behaves as a black box, providing accurate, but opaque, predictions. Here, the authors show that the synergy of cutting-edge ANNs with the rigor of Quantum Chemical Topology can result in Explainable Chemical AI (XCAI). [ABSTRACT FROM AUTHOR]

Mastering of analytical methods for accurate quantitative determinations of enantiomeric excess is a crucial aspect in asymmetric catalysis, chiral synthesis, and pharmaceutical applications. In this context, the phenomenon of Self‐Induced Diastereomeric Anisochronism (SIDA) can be exploited in NMR spectroscopy for accurate determinations of enantiomeric composition, without using a chiral auxiliary that could interfere with the spectroscopic investigation. This phenomenon can be particularly useful for improving the quantitative analysis of mixtures with low enantiomeric excesses, where direct integration of signals can be tricky. Here, we describe a novel analysis protocol to correctly determine the enantiomeric composition of scalemic mixtures and investigate the thermodynamic and stereochemical features at the basis of SIDA. Dipeptide derivatives were chosen as substrates for this study, given their central role in drug design. By integrating the experiments with a conformational stochastic search that includes entropic contributions, we provide valuable information on the dimerization thermodynamics, the nature of non‐covalent interactions leading to self‐association, and the differences in the chemical environment responsible for the anisochronism, highlighting the importance of different stereochemical arrangement and tight association for the distinction between homochiral and heterochiral adducts. An important role played by the counterion was pointed out by computational studies. [ABSTRACT FROM AUTHOR]

This study delves into the intricate interactions between surface‐near species, OH and H2O, on electrodes in electrochemical interfaces. These species are an inevitable part of many electrocatalytic energy conversion reactions such as the oxygen reduction reaction. In our modeling, we utilize high statistics on a dataset of complex solid solutions with high atomic variability to show the emergence of H2O‐metal covalent bonds under specific conditions. Based on density functional theory (DFT) calculations of adsorption energies on many thousands of different surface compositions, we provide a quantifiable physical understanding of this induced water covalency, which is rooted in simple quantum mechanics. Directional hydrogen bonding between surface‐near H2O and OH, enables surface bonding electrons to delocalize, mediated by near‐symmetrical adsorbate resonance structures. The different adsorbate resonance structures differ by surface coordination explaining the induced H2O‐metal bonding. [ABSTRACT FROM AUTHOR]

Natural enediyne antibiotics undergo thermally induced cycloaromatization under physiological conditions to generate highly reactive diradicals that effectively abstract hydrogen atoms from biomacromolecules like DNA, leading to DNA cleavage and inducing programmed cell death in tumor cells, exhibiting potent cytotoxicity and broad‐spectrum antitumor properties. In order to further explore promising synthetic analogues of natural enediynes, a 3,6‐pyridazinedione moiety was fused at the ene‐position, and 10 new enediyne compounds with different atoms at the propargylic positions were synthesized. Differential scanning calorimetry (DSC) and electron paramagnetic resonance results indicated that these enediynes exhibited a rather low onset temperature and the capability to generate radical species at physiological temperatures. Density functional theory (DFT) calculations demonstrated that the pyridazinedione moiety facilitates cascade rearrangement processes, thereby endowing the key cycloaromatization with a low energy barrier. The enediynes with propargylic oxygen exhibit strong DNA cleavage capabilities, and all pyridazinedione‐fused enediynes showed IC50 values in the range of several tens of micromolars against HeLa cells. [ABSTRACT FROM AUTHOR]

The rapid depletion of fossil fuels and the change from conventional energy supply to so‐called sustainable and renewable energy sources have led to a renaissance of electrochemical, photochemical, and photoelectrochemical methods for chemical synthesis. While drastic experimental improvements have been realized in recent years, systematic computational studies of these types of reactions are, however, rather limited caused by a lack of suitable representations. Herein we present a generalized method to investigate and analyze a chemical system with respect to its redox‐ and acid/base‐properties based on Gibbs free‐energy differences. We represent the results in a clear manner by means of redox−acid/base phase diagrams. Motivated by computational needs, the presented method is a direct link between experimentally measurable values and Gibbs free‐energy profiles, connecting experiment and simulation. Thus, it serves as an entry to systematic computational studies of reactions, which involve a combination of electron transfers and acid/base‐chemical reaction steps, because it enables the representation of both thermodynamic and kinetic properties. The presented method is applied to four exemplary systems: Phenol, dicobaltocenium amine as a proton‐coupled electron transfer (PCET) reactant, and two porphyrin NiII catalysts for the electrocatalytic hydrogen evolution reaction (HER). [ABSTRACT FROM AUTHOR]

The integrative implementation of multiple different components into metallosupramolecular self‐assemblies requires sophisticated strategies to avoid the formation of statistical mixtures. Previously, the key focus was set on thermodynamically driven reactions of simple homoleptic into complex heteroleptic structures. Using Pd2LA2LB2‐type coordination cages, we herein show that integrative self‐sorting can be reversed by a change of solvent (from DMSO to MeCN) to favor narcissistic re‐segregation into coexisting homoleptic species Pd2LA4 and Pd3LB6. Full separation (“unsorting”) back to a mixture of the homoleptic precursors was finally achieved by selective precipitation of Pd3LB6 with anionic guest G1 from MeCN, keeping pure Pd2LA4 in solution. When a mixture of homoleptic Pd3LB6 and heteroleptic Pd2LA2LB2 is exposed to a combination of two different di‐anions (G1 and G2) in DMSO, selective guest uptake gives rise to two defined coexisting host–guest complexes. A joint experimental and deep theoretical investigation via liquid‐state integral equation theory of the reaction thermodynamics on a molecular level accompanied by solvent distribution analysis hints at solvent expulsion from Pd2LA4 to favor the formation of Pd2LA2LB2 in DMSO as the key entropic factor for determining the solvent‐specific modulation of the cage conversion equilibrium. [ABSTRACT FROM AUTHOR]

During the last decade, thermally activated delayed fluorescence has been the topic of intense research due to its great potential for highly efficient all organic light emitting devices. While the quinazoline heterocycle has been used for its electron withdrawing ability in various push‐pull dyes, quinazoline derivatives have been rarely considered for TADF emitters. Here we design and synthesize a new series of dyes with phenyl‐ or methoxy‐substituted quinazoline rings combined with 9,9‐dimethylacridan, phenoxazine or phenothiazine and either 1,4‐phenylene or 2,5‐thienylene as linker. Combining optical spectroscopy and theoretical investigations, we demonstrate that delayed fluorescence is observed in the solid state for the phenyl‐substituted quinazoline derivatives for which both quasi‐equatorial and quasi‐axial conformations are predicted to coexist. Calculations further suggest that the reversed intersystem crossing is likely to involve the second triplet state that shows small energy splitting with the first singlet state. These results prompt further investigations of phenyl‐substituted quinazoline based dyes for TADF. [ABSTRACT FROM AUTHOR]

Background: As large language models continue to expand in size and diversity, their substantial potential and the relevance of their applications are increasingly being acknowledged. The rapid advancement of these models also holds profound implications for the long-term design of stimulus-responsive materials used in drug delivery. Methods: The large model used Hugging Face's Transformers package with BigBird, Gemma, and GPT NeoX architectures. Pre-training used the PubChem dataset, and fine-tuning used QM7b. Chemist instruction training was based on Direct Preference Optimization. Drug Likeness, Synthetic Accessibility, and PageRank Scores were used to filter molecules. All computational chemistry simulations were performed using ORCA and Time-Dependent Density-Functional Theory. Results: To optimize large models for extensive dataset processing and comprehensive learning akin to a chemist's intuition, the integration of deeper chemical insights is imperative. Our study initially compared the performance of BigBird, Gemma, GPT NeoX, and others, specifically focusing on the design of photoresponsive drug delivery molecules. We gathered excitation energy data through computational chemistry tools and further investigated light-driven isomerization reactions as a critical mechanism in drug delivery. Additionally, we explored the effectiveness of incorporating human feedback into reinforcement learning to imbue large models with chemical intuition, enhancing their understanding of relationships involving -N=N- groups in the photoisomerization transitions of photoresponsive molecules. Conclusions: We implemented an efficient design process based on structural knowledge and data, driven by large language model technology, to obtain a candidate dataset of specific photoswitchable molecules. However, the lack of specialized domain datasets remains a challenge for maximizing model performance. [ABSTRACT FROM AUTHOR]

Using fine organic synthesis methods and the latest capabilities of computational chemistry, for the first time, 2-(4-amino-6-phenyl-1,2,5,6-tetrahydro-1,3,5-triazine-2(1H)-ylidene)-malononitrile, which contains a triazine ring and various functional groups in the molecule, was synthesized without the presence of a catalyst through a one-step three-component reaction. Structural analyses were carried out using X-ray and Hirshfeld surface analysis, and it was determined that asymmetric molecules of 2-(4-amino-6-phenyl1,2,5,6-tetrahydro-1,3,5-triazine-2(1H)-ylidene)-malononitrile and a dimethylformamide solvent molecule in the asymmetric unit crystallize as a racemate in the monoclinic P21/c space group. These molecules exhibit stereochemical activity at the carbon atoms where the triazine rings are attached to the phenyl ring. For the first time, theoretical predictions using Density Functional Theory calculations for the synthesis reaction of tetrahydro-s-triazine via a one-step three-component condensation without a catalyst were experimentally tested. Comprehensive theoretical-experimental studies were conducted using quantum chemical calculations to simulate these chemical reactions, leading to significant results. The synthesis under milder conditions and atom-efficient methods obtained through computer simulations further enhances the importance of this work. This compound has shown high inhibitory effects on acetylcholinesterase in living organisms, and multiple analyses, including "molecular docking" studies, have revealed that in the future, representatives of these inhibitors with different functional groups could potentially be used as drugs against epilepsy, Alzheimer’s, and other neurological diseases. [ABSTRACT FROM AUTHOR]

Context: Alzheimer's disease (AD) is the leading cause of dementia around the world, totaling about 55 million cases, with an estimated growth to 74.7 million cases in 2030, which makes its treatment widely desired. Several studies and strategies are being developed considering the main theories regarding its origin since it is not yet fully understood. Among these strategies, the 5-HT6 receptor antagonism emerges as an auspicious and viable symptomatic treatment approach for AD. The 5-HT6 receptor belongs to the G protein-coupled receptor (GPCR) family and is closely implicated in memory loss processes. As a serotonin receptor, it plays an important role in cognitive function. Consequently, targeting this receptor presents a compelling therapeutic opportunity. By employing antagonists to block its activity, the 5-HT6 receptor's functions can be effectively modulated, leading to potential improvements in cognition and memory. Methods: Addressing this challenge, our research explored a promising avenue in drug discovery for AD, employing Artificial Neural Networks–Quantitative Structure-Activity Relationship (ANN-QSAR) models. These models have demonstrated great potential in predicting the biological activity of compounds based on their molecular structures. By harnessing the capabilities of machine learning and computational chemistry, we aimed to create a systematic approach for analyzing and forecasting the activity of potential drug candidates, thus streamlining the drug discovery process. We assembled a diverse set of compounds targeting this receptor and utilized density functional theory (DFT) calculations to extract essential molecular descriptors, effectively representing the structural features of the compounds. Subsequently, these molecular descriptors served as input for training the ANN-QSAR models alongside corresponding biological activity data, enabling us to predict the potential efficacy of novel compounds as 5-hydroxytryptamine receptor 6 (5-HT6) antagonists. Through extensive analysis and validation of ANN-QSAR models, we identified eight new promising compounds with therapeutic potential against AD. [ABSTRACT FROM AUTHOR]

Context: An inclusion complex between 2,2′-azobis(2-methylpropionamidine) dihydrochloride (AAPH), a widely employed azocompound, and cucurbit[7]util (CB[7]), has shown an increased yield of radicals derived from the homolytic cleavage of the azo bond. Aimed to get insights about the formation of complexes and their effect on the yield of radicals production, complexes of CB[7] with seven azocompounds were studied by computational methods. Molecular electrostatic surfaces and structural analysis showed that the inclusion of symmetrical azocompounds inside of CB[7] depends mainly on the charge density and position of the functional groups at the main chain of the azoderivative. Analysis of non-covalent interactions and thermodynamic outcomes revealed that positively charged azocompounds with amidinium or imidazolium groups presented strong favorable interactions (multiple hydrogen bonds) with the oxygens of CB[7] portals. Additionally, carbon-centered radicals generated from the complexes (azocompounds@CB[7]) were corroborated using the electron localization function (ELF). Results evidenced that the strength of the interactions and the level of inclusion (partial or complete) between the azocompound and CB[7] determined the final orientation of the radicals (located out- or inside of the CB[7] cavity). Obtained results could be employed to design new supramolecular systems based on the properties of azocomplound@CB[7] complexes for new scientific or industrial applications. Methods: First-principles calculations at B3LYP-D3BJ/6-311g(d,p) level theory in the gas phase and in solvent (PCM, water) were performed in Gaussian 16 software package. The dispersion energy correction was included through the Grimme's dispersion with Becke-Johnson damping D3(BJ). Thermodynamical data and the minimum character of all structures were obtained from vibrational frequency calculations. NBO, Multiwfn, Chemcraft, and NCIPLOT software were used to perform population analysis, analyze outcomes, visualize data, and display non-covalent interactions respectively. [ABSTRACT FROM AUTHOR]

Paddlanes are tricyclic molecules with four bridging chains that share the same two bridgehead carbon atoms. The smallest known paddlane containing only carbon atoms in the main framework has a total of 18 atoms. We utilized computational chemistry (MP2/cc‐pVTZ) to locate the smallest possible all‐carbon paddlanes that meet the criterion suggested by Hoffmann, Schleyer, and Schaefer as being "fleeting," meaning frequency calculations suggest the structures are energy minima on their respective potential energy surfaces (PES). Our results suggest that paddlane compounds with a total of 10 or fewer atoms can be considered to be non‐viable either because they are not energy minima on the PES or due to strain in the system that manifests as especially long carbon–carbon bonds. Some paddlanes with a total of 11 carbon atoms proved to be energy minima but still exhibited longer carbon–carbon bonds than normal. Finally, several paddlanes with a total of 12 carbons appear to be the least strained since carbon–carbon bond lengths remain reasonable. [ABSTRACT FROM AUTHOR]

Native mass spectrometry of membrane proteins relies on non‐ionic detergents which protect the protein during transfer from solution into the gas phase. Once in the gas phase, the detergent micelle must be efficiently removed, which is usually achieved by collision‐induced dissociation (CID). Recently, infrared multiple photon dissociation (IRMPD) has emerged as an alternative activation method for the analysis of membrane proteins, which has led to a growing interest in detergents that efficiently absorb infrared light. Here we investigate whether the absorption properties of synthetic detergents can be tailored by merging structural motifs of existing detergents into new hybrid detergents. We combine gas‐phase infrared ion spectroscopy with density functional theory to investigate and rationalize the absorption properties of three established detergents and two hybrid detergents with fused headgroups. We show that, although the basic intramolecular interactions in the parent and hybrid detergents are similar, the three‐dimensional structures differ significantly and so do the infrared spectra. Our results outline a roadmap for guiding the synthesis of tailored detergents with computational chemistry for future mass spectrometry applications. [ABSTRACT FROM AUTHOR]

A microwave‐assisted synthesis of 7‐amino‐1,2,4‐triazolo[1,5‐a][1,3,5]triazine‐2‐propanamides was developed using a three‐component, catalyst‐free reaction of cyanamide and trimethyl orthoformate with 3‐(5‐amino‐1H‐1,2,4‐triazol‐3‐yl)propanamides (3). The reaction tolerated structurally diverse substrates and proceeded chemo‐ and regio‐selectively, affording the target compounds in high purity in 5–10 minutes. The convenient chromatography‐free isolation and purification of the products add practicality to this method. The structural features of the prepared compounds were investigated using dynamic NMR spectroscopy, X‐ray crystallography and computational chemistry calculations. X‐ray crystallography performed on a representative compound, 3‐(7‐amino‐1,2,4‐triazolo[1,5‐a][1,3,5]triazin‐2‐yl)‐N‐(4‐benzyl)propanamide (4 l), showed the overall molecular conformation to adopt the shape of the letter C. Notable localisation of π‐electron density is found within the 1,2,4‐triazolo[1,5‐a][1,3,5]triazine system; a relatively short C−NH2 bond is consistent with restricted rotation about this bond. This study also presents a detailed analysis of the molecular interactions in 4 l using DFT and QTAIM methods with a focus on the hydrogen‐bonding and π‐stacking interactions that influence the molecular packing of 4 l. The findings reveal the significant roles of N−H⋅O, N−H⋅N and C−H⋅N interactions, along with electrostatically enhanced π⋅π contacts. A broad screening for insecticidal, fungicidal and herbicidal properties identified several compounds with potent herbicidal activity against Matricaria inodora. [ABSTRACT FROM AUTHOR]

In the 1H‐NMR spectra of a series of N‐1 substituted 4‐substituted‐1H‐1,2,3‐triazoles that have been prepared, the lone heterocyclic ring hydrogen (H‐5) appears as a singlet in all cases except those compounds that contain a 2‐fluorophenyl moiety at Position 4. In those cases, H‐5 is a doublet with J ~3.7 Hz. Based on computational chemistry results and geometric considerations, we attribute this splitting to through‐space H‐F coupling. [ABSTRACT FROM AUTHOR]