Molecular Geometry Optimization Research Articles

Crystal structure, Hirshfeld surface analysis and DFT studies of N-(4-acetylphenyl)quinoline-3-carboxamide

Crystalline organic compound, N-(4-acetylphenyl)quinoline-3-carboxamide (4) was readily prepared using our previously reported experimental procedure by the reaction of quinoline-3-carboxylic acid (1) with thionyl chloride to generate the acid chloride in situ followed by the coupling itself with 4-aminoacetophenone (3). This report describes the in-depth structural analysis thereof. The spectral characterization was done using FT-IR, 1H-NMR, 13C-NMR and UV-Vis spectroscopy, while its 3D-structure confirmed unambiguously by single crystal X-ray diffraction. The title compound crystallizes in the monoclinic system with the P21/c space group, Z = 4. The crystal packing is mainly controlled by N–H•••O, and C–H•••O hydrogen bond interactions. Molecular geometry optimizations were conducted using the DFT method at the B3LYP/6-31G+(d,p) level of theory and the theoretical UV-Vis spectrum was found to be in good agreement with the experimental spectrum.

DFT-Based Study of Physical, Chemical and Electronic Behavior of Liquid Crystals of Azoxybenzene Group: <i>p</i>-azoxyanisole, <i>p</i>-azoxyphenetole, ethyl-<i>p</i>-azoxybenzoate, ethyl-<i>p</i>-azoxycinnamate and <i>n</i>-octyl-<i>p</i>-azoxycinnamate

The present work describes the geometry and electronic structures of liquid crystals of azoxybenzene group and their reactivity with respect to molecular properties: total energy, ionization potential, electron affinity, HOMO energy, LUMO energy, electronegativity, hardness and dipole moment. Literature shows that mesomorphism depends particularly on the nature of terminal groups and their linkages with parent molecule. And thus substitution of terminal groups can help to fine tune the liquid crystal behavior and also their applications. In this work the effect of four terminal groups of same and diverse nature has been studied. For the study, the molecular modeling and geometry optimization of the compounds have been performed on workspace program of CAChe Pro 5.04 software of Fujitsu using DFT method.

Nuclear-electronic orbital methods: Foundations and prospects.

The incorporation of nuclear quantum effects and non-Born-Oppenheimer behavior into quantum chemistry calculations and molecular dynamics simulations is a longstanding challenge. The nuclear-electronic orbital (NEO) approach treats specified nuclei, typically protons, quantum mechanically on the same level as the electrons with wave function and density functional theory methods. This approach inherently includes nuclear delocalization and zero-point energy in molecular energy calculations, geometry optimizations, reaction paths, and dynamics. It can also provide accurate descriptions of excited electronic, vibrational, and vibronic states as well as nuclear tunneling and nonadiabatic dynamics. Nonequilibrium nuclear-electronic dynamics simulations beyond the Born-Oppenheimer approximation can be used to investigate a wide range of excited state processes. This Perspective provides an overview of the foundational NEO methods and enumerates the prospects for using these methods as building blocks for future developments. The conceptual simplicity and computational efficiency of the NEO approach will enhance its accessibility and applicability to diverse chemical and biological systems.

Bioconcentration Factor of Polychlorinated Biphenyls and Its Correlation with UV- and IR-Spectroscopic data: A DFT based Study

Polychlorinated biphenyls (PCBs) are important class of persist organic pollutants that were used as a component of paints especially in printings, as plastificator of plastics and insulating materials in transformers and capacitors, heat transfer fluids, additives in hydraulic fluids in vacuum and turbine pumps. There is always a need to establish reliable procedures for predicting the bioconcentration potential of chemicals from the knowledge of their molecular structure, or from readily measurable properties of the substance. Hence, correlation and prediction of bio-concentration factors (BCFs) based on λmax and vibration frequencies of various bonds viz υ(C-H) and υ(C=C) of biphenyl and its fifty-seven derivatives have been made. For the study, the molecular modeling and geometry optimization of the PCBs have been performed on workspace program of CAChe Pro 5.04 software of Fujitsu using DFT method. UV-visible spectra for each compound were created by electron transition between molecular orbitals as electromagnetic radiation in the visible and ultraviolet (UV-visible) region is absorbed by the molecule. The energies of excited electronic states were computed quantum mechanically. IR spectra of transitions for each compound were created by coordinated motions of the atoms as electromagnetic radiation in the infrared region is absorbed by the molecule. The force necessary to distort the molecule was computed quantum mechanically from its equilibrium geometry and thus frequency of vibrational transitions was predicted. Project Leader Program associated with CAChe has been used for multiple linear regression (MLR) analysis using above spectroscopic data as independent variables and BCFs of PCBs as dependent variables. The reliability of correlation and predicting ability of the MLR equations (models) are judged by R2, R2adj, se, q2L10O and F values. This study reflected clearly that UV and IR spectroscopic data can be used to predict BCFs of a large number of related compounds within limited time without any difficulty.

Iterative Power Algorithm for Global Optimization with Quantics Tensor Trains.

Optimization algorithms play a central role in chemistry since optimization is the computational keystone of most molecular and electronic structure calculations. Herein, we introduce the iterative power algorithm (IPA) for global optimization and a formal proof of convergence for both discrete and continuous global search problems, which is essential for applications in chemistry such as molecular geometry optimization. IPA implements the power iteration method in quantics tensor train (QTT) representations. Analogous to the imaginary time propagation method with infinite mass, IPA starts with an initial probability distribution ρ0(x) and iteratively applies the recurrence relation ρk+1(x) = U(x) ρk(x)/∥Uρk∥L1, where U(x) = e-V(x) is defined in terms of the potential energy surface (PES) V(x) with global minimum at x = x*. Upon convergence, the probability distribution becomes a delta function δ(x - x*), so the global minimum can be obtained as the position expectation value x* = Tr[x δ(x - x*)]. QTT representations of V(x) and ρ(x) are generated by fast adaptive interpolation of multidimensional arrays to bypass the curse of dimensionality and the need to evaluate V(x) for all possible values of x. We illustrate the capabilities of IPA for global search optimization of two multidimensional PESs, including a differentiable model PES of a DNA chain with D = 50 adenine-thymine base pairs, and a discrete non-differentiable potential energy surface, V(p) = mod(N,p), that resolves the prime factors of an integer N, with p in the space of prime numbers {2, 3,..., pmax} folded as a d-dimensional 21 × 22 × ··· × 2d tensor. We find that IPA resolves multiple degenerate global minima even when separated by large energy barriers in the highly rugged landscape of the potentials. Therefore, IPA should be of great interest for a wide range of other optimization problems ubiquitous in molecular and electronic structure calculations.

DFT, docking, MD simulation, and vibrational spectra with SERS analysis of a benzoxazole derivative: an anti-cancerous drug

Spectroscopic, DFT, and SERS studies of antimicrobial bioactive 2-(p-bromophenyl)-5-(2-(4-(p-chlorophenyl)piperazine-1-yl)acetamido)benzoxazole (BCAB) have been reported. Very large changes are seen wavenumbers in Raman and SERS. Variations in modes may be due to surface π-electron interactions and means, the BACB is inclined with respect to the metal surface. Theoretical molecular geometry optimization parameters, wavenumbers, frontier molecular orbitals, and molecular electrostatic potential surface have been calculated using density functional theory. The docked ligand forms a stable complex with SOCS-2 and can be BCAB may be an anti-cancerous drug. According to RMSD, RMSF, and Rg analysis, BACB and SOCS-2 protein form a stable and stable interaction.

Synthesis, antibacterial and computational studies of Halo Chalcone hybrids from 1-(2,3-Dihydrobenzo[b][1,4]dioxin-6-yl)ethan-1-one

In an endeavor to develop antibacterial agents, a series of six 1,4-benzodioxan-6-yl substituted chalcone derivatives were synthesized by the base-catalyzed Claisen-Schmidt reaction of the 1-(2,3-dihydrobenzo[b][1,4]dioxin-6-yl)ethan-1-one with fluoro and chloro substituted aromatic aldehydes. The synthesized products were characterized by FT-IR, 1H NMR, and 13C NMR spectroscopic techniques. The density functional theory (DFT) calculations were performed using the B3LYP functional with the 6-31G(d,p) basis set for the optimization of molecular geometries and frequency calculations. The CAM-B3LYP functional with a 6-31G(d,p) basis set was used in time-dependent density functional theory (TD-DFT) calculations for the electronic absorption studies. Optimized geometries, frontier molecular orbitals, and global reactivity descriptors' specifications were computed and addressed. The simulated electronic absorption spectra were recorded in the gas phase and dichloromethane (DCM) solvent. The electronic configurations, oscillator strengths, and excited state energies were also discussed. The theoretical UV–Vis and IR vibrational analyses were equated with the experimental findings for the assignment of absorption bands. The synthesized chalcones were evaluated for in vitro antibacterial activities against two Gram positive bacteria (Bacillus subtilis and Staphylococcus aureus) and two Gram negative bacteria (Escherichiacoli and Proteus vulgaris). The DFT simulations were correlated with the antibacterial findings and it was discovered that they were highly helpful in the designing antibacterial agents and to establish the structure–activity relationship. Theoretical calculations are in good correlation with the in vitro antibacterial results.

Second-Order Orbital Optimization with Large Active Spaces Using Adaptive Sampling Configuration Interaction (ASCI) and Its Application to Molecular Geometry Optimization.

Recently, selected configuration interaction (SCI) methods that enable calculations with several tens of active orbitals have been developed. With the SCI subspace embedded in the mean field, molecular orbitals with an accuracy comparable to that of the complete active space self-consistent field method can be obtained. Here, we implement the analytical gradient theory for the single-state adaptive sampling CI (ASCI) SCF method to enable molecular geometry optimization. The resulting analytical gradient is inherently approximate due to the dependence on the sampled determinants, but its accuracy was sufficient for performing geometry optimizations with large active spaces. To obtain the tight convergence needed for accurate analytical gradients, we combine the augmented Hessian (AH) and Werner-Meyer-Knowles (WMK) second-order orbital optimization methods with the ASCI-SCF method. We test these algorithms for orbital and geometry optimizations, demonstrate applications of the geometry optimizations of polyacenes and periacenes, and discuss the geometric dependence of the characteristics of singlet ASCI wave functions.

Electrochemical and theoretical investigation of functionalized reduced graphene aerogel modified electrode for lead ions sensing

In this work, we have incorporated 4,4′-methylenedianiline into reduced graphene oxide (F-rGO Aerogel) with high surface area and increased conductivity. The modified electrode with F-rGO Aerogel presents a sensitive Pb(II) sensor performance with good properties, such as excellent conductivity, high effective surface area and high adsorption ability for lead (II) ions. After the deposition process, the amount of lead on the working electrode surface can be increased due to easily coordination of the –NH2 groups present in F-rGO Aerogel/CPE with Pb(II). Molecular geometry optimization, structural calculations, as well as molecular system electronic state were carried out to obtain suitable information about adsorption process. The range of interaction energies in the cationic complexes (−659.80 kJ mol−1 to −763.15 kJ mol−1) represents the establishment of a covalent bond, by sharing π-electron with d-orbitals of lead cations. The strong cation-π interaction with high adsorption energy helps to remain the Pb2+ ions on the graphene surface through chemical adsorption that do not collapse with time. This strong chemisorption interaction allows the graphene species, especially the newly presented one, to be one of the most powerful adsorbent for adsorbing lead cation atoms. Under optimized condition, the built active electrode system could present a low detection limit of 0.03 μM concentration and a detection range of 0.01–0.3 μM. Furthermore, the reproducibility and stability of the sensor exhibited satisfactory results.

Density functional theory for the thermodynamic gas-phase investigation of butanol biofuel and its isomers mixed with gasoline and ethanol.

Herein, we present the results of our study on the thermodynamic properties of the isomers of butanol (n-butanol, 2-butanol, i-butanol, and t-butanol) to evaluate their thermodynamic potential as a complementary biofuel and/or substitute for ethanol and gasoline. The Gaussian09W software was used to perform molecular geometry optimization calculations using density functional theory with the B3lyp hybrid function using the base set 6-311++g(d,p) and the compound methods G3, G4, and CBS-QB3. Calculations of the fundamental frequency of the molecules were performed to obtain the molecular vibration modes for the respective frequencies. These calculations provided thermodynamic parameters such as the entropy, enthalpy, and specific molar heat at constant pressure, all as a function of the temperature. The parameter values obtained by each method were compared to the experimental values available in the literature. The results showed good accuracy, especially those obtained at the B3lyp/6-311++g(d,p) level for n-butanol. The error between the theoretical and experimental values for the combustion enthalpy of n-butanol was less than 4% at 298.15K; due to the good prediction of its thermodynamic properties, we used n-butanol as a model for the prediction of other thermodynamic properties. We started a molecular docking study of four ligands, namely, n-butanol, ethanol, propanol, heptane, isooctane, and methanol interacting with butanol isomers. The highest values of affinity energy found were for N-butanol. The possible formation of hydrogen bonds, associations by means of London forces, hydrogen, and alkyl interactions were analyzed. n-Butanol was added to ethanol-gasoline mixtures in the temperature range of 298.15 to 600K and the results suggest that n-butanol has a higher calorific value than gasoline-ethanol mixtures in G30E, G40E, G50E, G60E, G70E, G80E, G90E, and E100 blends. As such, n-butanol releases greater amounts of heat during combustion and is thus a viable alternative to biofuels.

Learning to Optimize Molecular Geometries Using Reinforcement Learning.

Though quasi-Newton methods have been widely adopted in computational chemistry software for molecular geometry optimization, it is well known that these methods might not perform well for initial guess geometries far away from the local minima, where the quadratic approximation might be inaccurate. We propose a reinforcement learning approach to develop a model that produces a correction term for the quasi-Newton step calculated with the BFGS algorithm to improve the overall optimization performance. Our model is able to complete the optimization in about 30% fewer steps than pure BFGS for molecules starting from perturbed geometries. The new method has similar convergence to BFGS when complemented with a line search procedure, but it is much faster since it avoids the multiple gradient evaluations associated with line searches.

Photophysics, photochemistry and bioimaging application of 8-azapurine derivatives.

New 2-aryl-1,2,3-triazolopyrimidines were designed, synthesized, and characterized. Their optical properties were thoroughly studied in the solid phase, in solution and in a biological environment. Density Functional Theory (DFT) based calculations were performed, including the molecular geometry optimization for both the ground state and the first singlet excited state, the prediction of the UV-Vis absorption and fluorescence spectra, the determination of the molecular electrostatic properties and the solvent effect on the optical properties. The emission intensity was revealed to increase in time upon irradiation. Mass spectrometric research, quantum mechanical calculations, and analysis of literature data suggested a possible photo-transformation pathway through the homolytic cleavage of one of the C-Cl bonds upon irradiation with UV light. The structure of the active intermediate was identified by the series of mass spectrometry experiments and via synthesis of putative transformation products. The kinetic parameters measured in different solvents allowed estimating the rate of these photo-transformations. Biological experiments demonstrated that 2-aryl-1,2,3-triazolopyrimidines penetrate cells and selectively accumulate in the cell membrane and the Golgi complex and endoplasmic reticulum. Their unique properties pave the way for new possible applications of fluorescent 8-azapurines in biology and medicine.

Restricted-Variance Constrained, Reaction Path, and Transition State Molecular Optimizations Using Gradient-Enhanced Kriging

Gaussian process regression has recently been explored as an alternative to standard surrogate models in molecular equilibrium geometry optimization. In particular, the gradient-enhanced Kriging approach in association with internal coordinates, restricted-variance optimization, and an efficient and fast estimate of hyperparameters has demonstrated performance on par or better than standard methods. In this report, we extend the approach to constrained optimizations and transition states and benchmark it for a set of reactions. We compare the performance of the newly developed method with the standard techniques in the location of transition states and in constrained optimizations, both isolated and in the context of reaction path computation. The results show that the method outperforms the current standard in efficiency as well as in robustness.

Microsolvation of heavy halides

AbstractThe fundamental question of how intermolecular interactions lead to the stabilization of heavy halides (Br−, I−, At−) microsolvated with up to six explicit water molecules is addressed here. An exhaustive exploration of the potential energy surfaces using a random search algorithm followed by optimization of molecular geometries using pseudopotentials and at the full four component relativistic levels of theory, affords a good number of structures with high probabilities of occurrence, highlighting the important role of local minima to reproduce experimentally measured properties. Sequential hydration enthalpies for astatide are reported here for the first time in the scientific literature. Closed shell (ionic, long range) as well as intermediate character interactions (contributions from closed shell and covalent) are at play stabilizing the clusters. The ability of water molecules to either donate or to accept electron density dictates the nature and strength of the corresponding hydrogen bonds in solvation shells. Binding energies and molecular geometries are shown to be more sensitive to electron correlation than to relativistic effects.

Synthesis and Evaluation of Non-Hydrolyzable Phospho-Lysine Peptide Mimics.

The intrinsic lability of the phosphoramidate P−N bond in phosphorylated histidine (pHis), arginine (pHis) and lysine (pLys) residues is a significant challenge for the investigation of these post‐translational modifications (PTMs), which gained attention rather recently. While stable mimics of pHis and pArg have contributed to study protein substrate interactions or to generate antibodies for enrichment as well as detection, no such analogue has been reported yet for pLys. This work reports the synthesis and evaluation of two pLys mimics, a phosphonate and a phosphate derivative, which can easily be incorporated into peptides using standard fluorenyl‐methyloxycarbonyl‐ (Fmoc‐)based solid‐phase peptide synthesis (SPPS). In order to compare the biophysical properties of natural pLys with our synthetic mimics, the pK a values of pLys and analogues were determined in titration experiments applying nuclear magnetic resonance (NMR) spectroscopy in small model peptides. These results were used to compute electrostatic potential (ESP) surfaces obtained after molecular geometry optimization. These findings indicate the potential of the designed non‐hydrolyzable, phosphonate‐based mimic for pLys in various proteomic approaches.

Synthesis, Characterization, Spectroscopic, DFT and Molecular Docking Studies of 3-(3,4-Dihydroxyphenyl)-1-Phenyl-3-(Phenylamino)Propan-1-One

The present work elucidates the synthesis, characterization of 3-(3,4-dihydroxyphenyl)-1-phenyl-3-(phenylamino)propan-1-onethrough Mannich type reaction using 1-hexyltriazolium methanesulfonate ionic liquid as catalyst. The synthesized molecule was functionally and structurally elucidated utilizing analytical techniques such as FT-IR, NMR, Mass and HPLC analysis were also carried out to establish the purity of target molecule. The computational studies, via optimization of molecular geometry, simulated vibrational frequency, NMR chemical shifts, UV-Vis absorbance, different partial atomic charges, surface analyses, FMO analyses, Fukui functions, reactivity descriptors, thermochemistry, NLO property, NBO analyses were also carried out using B3LYP/6-31G(d,p) basic set. The comparisons of all the experimental and their simulated spectral data for the target compound was made and which show good agreements with one another. All the computational attempts of the target molecule were made with the deep insights toward their future scope comprising with chemical reactivity, biological property, optical device applications, and so on. In addition, the molecular docking studies were also been carried out with three selected protein and the results reveals that the target compound is potential to interact with influenza neuraminidase and chikungunya nsP2 protease enzymes.

Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials

Machine learning techniques allow a direct mapping of atomic positions and nuclear charges to the potential energy surface with almost ab initio accuracy and the computational efficiency of empirical potentials. In this work, we propose a machine learning method for constructing high-dimensional potential energy surfaces based on feed-forward neural networks. As input to the neural network, we propose an extendable invariant local molecular descriptor constructed from geometric moments. Their formulation via pairwise distance vectors and tensor contractions allows a very efficient implementation on graphical processing units (GPUs). The atomic species is encoded in the molecular descriptor, which allows the restriction to one neural network for the training of all atomic species in the data set. We demonstrate that the accuracy of the developed approach in representing both chemical and configurational spaces is comparable to the one of several established machine learning models. Due to its high accuracy and efficiency, the proposed machine-learned potentials can be used for any further tasks, for example, the optimization of molecular geometries, the calculation of rate constants, or molecular dynamics.

An approximation strategy to compute accurate initial density matrices for repeated self-consistent field calculations at different geometries

ABSTRACT Repeated computations on the same molecular system, but with different geometries, are often performed in quantum chemistry, for instance, in ab-initio molecular dynamics simulations or geometry optimisations. While many efficient strategies exist to provide a good guess for the self-consistent field procedure, little is known on how to efficiently exploit the abundance of information generated during the many computations. In this article, we present a strategy to provide an accurate initial guess for the density matrix, expanded in a set of localised basis functions, within the self-consistent field iterations for parametrised Hartree–Fock problems where the nuclear coordinates are changed along with a few user-specified collective variables, such as the molecule's normal modes. Our approach is based on an offline-stage where the Hartree–Fock eigenvalue problem is solved for some particular parameter values and an online-stage where the initial guess is computed very efficiently for any new parameter value. The method allows nonlinear approximations of density matrices, which belong to a non-linear manifold that is isomorphic to the Grassmann manifold, by mapping such a manifold onto the tangent space. Numerical tests on different amino acids show promising initial results.

Restricted-Variance Molecular Geometry Optimization Based on Gradient-Enhanced Kriging

Machine learning techniques, specifically gradient-enhanced Kriging (GEK), have been implemented for molecular geometry optimization. GEK-based optimization has many advantages compared to conventional—step-restricted second-order truncated expansion—molecular optimization methods. In particular, the surrogate model given by GEK can have multiple stationary points, will smoothly converge to the exact model as the number of sample points increases, and contains an explicit expression for the expected error of the model function at an arbitrary point. Machine learning is, however, associated with abundance of data, contrary to the situation desired for efficient geometry optimizations. In this paper, we demonstrate how the GEK procedure can be utilized in a fashion such that in the presence of few data points, the surrogate surface will in a robust way guide the optimization to a minimum of a potential energy surface. In this respect, the GEK procedure will be used to mimic the behavior of a conventional second-order scheme but retaining the flexibility of the superior machine learning approach. Moreover, the expected error will be used in the optimizations to facilitate restricted-variance optimizations. A procedure which relates the eigenvalues of the approximate guessed Hessian with the individual characteristic lengths, used in the GEK model, reduces the number of empirical parameters to optimize to two: the value of the trend function and the maximum allowed variance. These parameters are determined using the extended Baker (e-Baker) and part of the Baker transition-state (Baker-TS) test suites as a training set. The so-created optimization procedure is tested using the e-Baker, full Baker-TS, and S22 test suites, at the density functional theory and second-order Møller–Plesset levels of approximation. The results show that the new method is generally of similar or better performance than a state-of-the-art conventional method, even for cases where no significant improvement was expected.

DEPENDENCE OF DYNAMIC VISCOSITY OF NAPHTHENIC HYDROCARBONS IN CYCLOPENTANE SERIES ON QUANTUM AND STRUCTURAL PARAMETERS

Interrelation of parameters for Newtonian fluid viscous flow of monocyclic hydrocarbons with quantum and structural (topological) characteristics of molecules are considered. Ionization potentials and topological indices, respectively, were considered as quantum and topological characteristics. The vertical ionization potentials were calculated by the Koopmans ' theorem of quantum chemical methods with full molecular geometry optimization. We have studied topological indices that take into account the size and shape of the molecular graph. As a topological descriptors the Wiener index, the Balaban centric index, the Randic index (the index of molecular connectivity), Gutman (Szeged) index, Platt index and the Harary index were considered. For hydrocarbons in cyclopentane series with side chains, the kinetic compensation effect of dynamic viscosity has been established, which connects the activation energy and Arrhenius factor in the framework of the Frenkel-Eyring model. It is established that for compounds of a number of five-membered naphthenes, the apparent activation energy for viscous flow and the associated pre-exponential factor, depends on quantum parameters (ionization potentials) and the topology of the molecules. In this paper, a regression quantitative structure−property relationship (QSPR) is proposed using as descriptors the ionization potentials and topological indices for the prediction of dynamic viscosity. Prognostics capabilities of the proposed model and the adequacy of the forecast were verified by calculating the values of the dynamic viscosity of hydrocarbons that are not included in the base series. Experimental and theoretical substantiation of the proposed regularity was given within the framework of the representation of the viscous flow activation energy as a measure of intermolecular interaction and the predominance of dispersion interaction in hydrocarbon molecules for cyclopentane series. The equation obtained during the study can be used to predict the viscosity characteristics of synthesized and natural five-membered naphthenes.