Combination Of Quantum Chemistry Research Articles

In silico study of therapeutic deep eutectic solvent for tetracaine liquid delivery

The development of a drug delivery liquid formulation of ester – based tetracaine local anaesthetic is synthesised in silico via the formation of a therapeutic deep eutectic solvent in combination with citral, a natural monoterpenoid. The properties of the obtained liquid system, studied at the nanoscopic level, are derived from a combination of quantum chemistry and classical molecular dynamics simulations. The intermolecular forces (hydrogen bonding) were analysed and their relationship with liquid phase properties were established. Water effect on the liquid phase properties were also considered in terms of competing for hydrogen bonding sites in the components of the developed fluid. The systems proposed in this work were studied using CIT (HBA) 1:1 TET (HBD) molar ratio at temperatures ranging from 288 K to 318 K and 1 bar. The hydrogen bond network developed between the oxygen atom in citral molecules and the –NH group in tetracaine molecules under those conditions, confirmed via minimal cluster analysis, is maintained during the addition of water up to 1.3 wt% as the water molecules do not disrupt the intermolecular hydrogen bond but develop new interactions (hydrogen bonds) with citral and tetracaine monomers presented in the fluid. The reported results probed the suitability of the considered system as an alternative drug delivery method as well as the possibility of developing deep eutectic-based formulation of anaesthetics using natural compounds due to the formation of effective and strong intermolecular hydrogen bonds.

Extraction mechanism analysis and energy saving enhancement of extraction separation of methyl tert-butyl ether and methanol by ionic liquid based on molecular dynamics simulation

• The ILs extractant is screened by quantum chemistry and MD simulation. • The process flow of applying ILs in the production of MTBE was designed. • The sequential iterative optimization integrating reaction conditions is proposed. • The TAC in MTBE production was reduced by 26.77% when applying ILs. Methyl tert-butyl ether as an excellent gasoline additive has been widely used. There are a lot of mixtures of Methyl tert-butyl ether and methanol in the process of Methyl tert-butyl ether production. In this work, the interaction between different ionic liquids and methyl tert butyl ether / methanol was studied by the combination of quantum chemistry and molecular dynamics. In addition, there is little difference between the experimental data and the MD simulation results, indicating that the extraction performance can be predicted by MD simulation even without the phase equilibrium data. The interaction energy, radial and spatial distribution functions, and self-diffusion coefficients are calculated based on the molecular dynamics simulation results. It is proved that anions play a key role in the extraction process. The process of synthesis of methyl tert-butyl ether with ionic liquid extraction as separation unit was studied and the optimum process parameters were determined. Methyl tert-butyl ether with mole fraction greater than 99.9% could be obtained by ionic liquid extraction, and the use of Ionic liquids liquid-liquid extraction can save 26.77% of total annual cost than extractive distillation using conventional solvents. It plays a guiding role in the selection of green solvent ionic liquids extractant and the energy saving of liquid-liquid extraction.

Heterogeneous Nucleation of Butanol on NaCl: A Computational Study of Temperature, Humidity, Seed Charge, and Seed Size Effects.

Using a combination of quantum chemistry and cluster size distribution dynamics, we study the heterogeneous nucleation of n-butanol and water onto sodium chloride (NaCl)10 seeds at different butanol saturation ratios and relative humidities. We also investigate how the heterogeneous nucleation of butanol is affected by the seed size through comparing (NaCl)5, (NaCl)10, and (NaCl)25 seeds and by seed electrical charge through comparing (Na10Cl9)+, (NaCl)10, and (Na9Cl10)− seeds. Butanol is a common working fluid for condensation particle counters used in atmospheric aerosol studies, and NaCl seeds are frequently used for calibration purposes and as model systems, for example, sea spray aerosol. In general, our simulations reproduce the experimentally observed trends for the NaCl–BuOH–H2O system, such as the increase of nucleation rate with relative humidity and with temperature (at constant supersaturation of butanol). Our results also provide molecular-level insights into the vapor–seed interactions driving the first steps of the heterogeneous nucleation process. The main purpose of this work is to show that theoretical studies can provide molecular understanding of initial steps of heterogeneous nucleation and that it is possible to find cost-effective yet accurate-enough combinations of methods for configurational sampling and energy evaluation to successfully model heterogeneous nucleation of multicomponent systems. In the future, we anticipate that such simulations can also be extended to chemically more complex seeds.

Structure and complexation energy of benzotrifuroxan–benzene molecular complex

Based on a combination of quantum chemistry and atom–atom potentials methods, we developed a technique for modeling the structure and estimating the complexation energy of a binary organic complex in gas and crystal phases. The efficiency of this technique was illustrated by an example of the benzotrifuroxan–benzene molecular complex. For both phases, the same molecular binary structure (π-stacking) was obtained with a parallel arrangement of the planes of BTF and benzene molecules and complexation energies of −11.9 and −11.7 kcal mol−1 for a gas phase and a crystal cluster, respectively.

Efficient absorption properties of surface grafted HEDP-HAP composites for Pb2+ and Cu2+: Experimental study and visualization study of interaction based on Becke surface analysis and independent gradient model

A simple hydrothermal method was employed to prepare the surface grafted 1-hydroxyethylidene-1,1-diphosphonic acid-hydroxyapatite (HEDP-HAP) composites, which were termed as 0.2HEDP-HAP, 0.5HEDP-HAP and 1HEDP-HAP respectively, according to the HEDP content. Adsorption experiments showed that the maximum adsorption capacity of 0.5HEDP-HAP for Cu2+ reached 168.9 mg/g, which was 4.19 times that of HAP, while the maximum adsorption capacity of 1HEDP-HAP for Pb2+ was as high as 1521 mg/g, which was approximately 10 times that of HAP. For the first time, the interaction mechanism of HEDP with HAP, or HEDP-HAP composite with the two heavy metal ions was illustrated by the combination of quantum chemistry (QC) calculation, quantitative analysis of molecular surface, Becke surface analysis and Independent Gradient Model (IGM) method. The active reaction sites, the types, relative strength and essence of the interactions were revealed based on the visual analysis of interactions. The results demonstrated that the phosphonic acid group (PO3H−) of HEDP2- can form hydrogen bonds with the phosphate group (PO43-) and the hydroxyl group (OH) of HAP. The oxygen atom of PO3H- provided lone pair electrons to form chelate with Ca2+ of HAP, which resulted in the strong affinity of HEDP with HAP. Moreover, the significant improvements of uptake of surface grafted HEDP-HAP for Cu2+ and Pb2+ were due to the formation of the ternary surface complexes.

Quantum Chemistry in the Age of Machine Learning.

As the quantum chemistry (QC) community embraces machine learning (ML), the number of new methods and applications based on the combination of QC and ML is surging. In this Perspective, a view of the current state of affairs in this new and exciting research field is offered, challenges of using machine learning in quantum chemistry applications are described, and potential future developments are outlined. Specifically, examples of how machine learning is used to improve the accuracy and accelerate quantum chemical research are shown. Generalization and classification of existing techniques are provided to ease the navigation in the sea of literature and to guide researchers entering the field. The emphasis of this Perspective is on supervised machine learning.

Why Purine Nucleoside Phosphorylase Ribosylates 2,6-Diamino-8-azapurine in Noncanonical Positions? A Molecular Modeling Study.

Protein nucleoside phosphorylase (PNP) is an enzyme that catalyzes a reversible conversion process (ribosylation and phosphorolysis) between nucleobases (purines) and their nucleosides. Experimental studies showed that calf PNP ribosylates purine analogues in specific positions: 2,6-diamino-8-azapurine in position 7 or 8 and 8-azaguanine in position 9 of the triazole ring. The reason for this phenomenon can be a result of different expositions of purine substrates to the channel leading to the binding site. This hypothesis was verified by the application of molecular modeling techniques to two complexes of purine analogues 2,6-diamino-azapurine, calf PNP (pdb-code: 1LVU), and 8-azaguanine, calf PNP (pdb-code: 2AI1). The results obtained with a combination of quantum chemistry, docking, and molecular dynamics methods showed qualitative validity of our hypothesis. Binding free energies of protein-ligand systems showed that most probable binding poses expose N8 nitrogen for 2,6-diamino-8-azapurine and N9 nitrogen for 8-azaguanine into the binding channel and ruled out the exposition of N9 for 2,6-diamino-8-azapurine and N7 for 8-azaguanine, partially in agreement with the experimental data. The other important result obtained in this study is a significantly higher population of the protonated form of crucial residue Glu-201 present in the binding pocket, compared to the standard protonation of free glutamic acid in solution. This result combined with populations of tautomeric forms of both investigated systems strongly suggests that 2,6-diamino-8-azapurine and 8-azaguanine are recognized by proteins with deprotonated and protonated Glu-201 residues, respectively. A comparison of computed binding poses of the investigated ligands to the inhibitors present in crystal structures suggests that the modification of the (S)-PMPDAP inhibitor, in which a 2-(phosphonomethoxy)propyl chain is attached at position 8 instead of position 9, might increase its binding affinity.

A Mixed Quantum Chemistry/Machine Learning Approach for the Fast and Accurate Prediction of Biochemical Redox Potentials and Its Large-Scale Application to 315 000 Redox Reactions.

A quantitative understanding of the thermodynamics of biochemical reactions is essential for accurately modeling metabolism. The group contribution method (GCM) is one of the most widely used approaches to estimate standard Gibbs energies and redox potentials of reactions for which no experimental measurements exist. Previous work has shown that quantum chemical predictions of biochemical thermodynamics are a promising approach to overcome the limitations of GCM. However, the quantum chemistry approach is significantly more expensive. Here, we use a combination of quantum chemistry and machine learning to obtain a fast and accurate method for predicting the thermodynamics of biochemical redox reactions. We focus on predicting the redox potentials of carbonyl functional group reductions to alcohols and amines, two of the most ubiquitous carbon redox transformations in biology. Our method relies on semiempirical quantum chemistry calculations calibrated with Gaussian process (GP) regression against available experimental data and results in higher predictive power than the GCM at low computational cost. Direct calibration of GCM and fingerprint-based predictions (without quantum chemistry) with GP regression also results in significant improvements in prediction accuracy, demonstrating the versatility of the approach. We design and implement a network expansion algorithm that iteratively reduces and oxidizes a set of natural seed metabolites and demonstrate the high-throughput applicability of our method by predicting the standard potentials of more than 315 000 redox reactions involving approximately 70 000 compounds. Additionally, we developed a novel fingerprint-based framework for detecting molecular environment motifs that are enriched or depleted across different regions of the redox potential landscape. We provide open access to all source code and data generated.

COSMOplex: self-consistent simulation of self-organizing inhomogeneous systems based on COSMO-RS.

During the past 20 years, the efficient combination of quantum chemical calculations with statistical thermodynamics by the COSMO-RS method has become an important alternative to force-field based simulations for the accurate prediction of free energies of molecules in liquid systems. While it was originally restricted to homogeneous liquids, it later has been extended to the prediction of the free energy of molecules in inhomogeneous systems such as micelles, biomembranes, or liquid interfaces, but these calculations were based on external input about the structure of the inhomogeneous system. Here we report the rigorous extension of COSMO-RS to a self-consistent prediction of the structure and the free energies of molecules in self-organizing inhomogeneous systems. This extends the application range to many new areas, such as the prediction of micellar structures and critical micelle concentrations, finite loading effects in micelles and biomembranes, the free energies and structure of liquid interfaces, microemulsions, and many more related topics, which often are of great practical importance.

High-Level Spectroscopy, Quantum Chemistry, and Catalysis: Not just a Passing Fad.

Quantum chemistry can be used as a powerful link between theory and experiment for studying reactions in all areas of catalysis. The key feature of this approach is the combination of quantum chemistry with a range of high-level spectroscopic methods. This allows for conclusions to be reached that neither theory nor experiment would have been able to obtain in isolation.

The role of solvent in the self-assembly of m-aminobenzoic acid: a density functional theory and molecular dynamics study

Solvent can have significant effects on the solution thermodynamics and crystallisation kinetics of organic compounds from solution. In the present work, the early stages of aggregation of the organic molecule m-aminobenzoic (mABA) in two different solvents, dimethyl sulfoxide (DMSO) and water were studied using a combination of quantum chemistry, molecular dynamics and metadynamics simulations. Density functional theory (B97-D and M06-2X) calculations with the continuum solvation SMD model were used to probe the potential energy surface of molecular clusters of m-aminobenzoic acid, (mABA)n (n = 2–4), locate their low-lying energy structures, and compute the Gibbs free energies of (mABA)n in solution. Starting from a large number of randomly generated candidate structures and by imposing the condition of minimum free energy in solution for the isomers of (mABA)n, we proved that the most stable dimer and tetramer in solution correspond to the classic carboxylic dimer π–π stacking synthon found in the crystalline form-II of mABA. Molecular dynamics simulations of mABA solutions at different concentrations reveal a significant solvent-dependent aggregation behaviour for mABA: in water, even at low concentrations, mABA molecules spontaneously form H-bonded π–π stacking molecular clusters, whereas in organosulfur solutions the molecules of mABA are in a more solvated state. Metadynamics simulations and microsolvation density functional theory calculations rationalize the low level of mABA aggregation in DMSO in terms of the energetic barrier for the desolvation of mABA molecules and formation of dimers, and the strength of mABA-solvent interactions, which are both larger in DMSO compared with water. This work shows that the solvent and its specific interaction with the organic solute molecules influences both the thermodynamics and kinetics of the molecular self-assembly process.

Reaction Kinetics of Meteoric Sodium Reservoirs in the Upper Atmosphere.

The gas-phase reactions of a selection of sodium-containing species with atmospheric constituents, relevant to the chemistry of meteor-ablated Na in the upper atmosphere, were studied in a fast flow tube using multiphoton ionization time-of-flight mass spectrometry. For the first time, unambiguous observations of NaO and NaOH in the gas phase under atmospheric conditions have been achieved. This enabled the direct measurement of the rate constants for the reactions of NaO with H2, H2O, and CO, and of NaOH with CO2, which at 300-310 K were found to be (at 2σ confidence level): k(NaO + H2O) = (2.4 ± 0.6) × 10(-10) cm(3) molecule (-1) s(-1), k(NaO + H2) = (4.9 ± 1.2) × 10(-12) cm(3) molecule (-1) s(-1), k(NaO + CO) = (9 ± 4) × 10(-11) cm(3) molecule (-1) s(-1), and k(NaOH + CO2 + M) = (7.6 ± 1.6) × 10(-29) cm(6) molecule (-2) s(-1) (P = 1-4 Torr). The NaO + H2 reaction was found to make NaOH with a branching ratio ≥ 99%. A combination of quantum chemistry and statistical rate theory calculations are used to interpret the reaction kinetics and extrapolate the atmospherically relevant experimental results to mesospheric temperatures and pressures. The NaO + H2O and NaOH + CO2 reactions act sequentially to provide the major atmospheric sink of meteoric Na and therefore have a significant impact on the underside of the Na layer in the terrestrial mesosphere: the newly determined rate constants shift the modeled peak to about 93 km, i.e., 2 km higher than observed by ground-based lidars. This highlights further uncertainties in the Na chemistry cycle such as the unknown rate constant of the NaOH + H reaction. The fast Na-recycling reaction between NaO and CO and a re-evaluated rate constant of the NaO + CO2 sink should be now considered in chemical models of the Martian Na layer.

Modeling absorption and fluorescence solvatochromism with QM/Classical approaches

The applications of solvatochromism have enormously increased in the last years especially when combined with the use of fluorescence‐based techniques. By conjugating solvatochromic fluorescent probes to biologically relevant systems, the functions, activities, and interactions of such species in the context of living systems can be studied with spatial and temporal resolution. It is therefore of large interest to have theoretical methods and computational tools able to predict the sensitivity in both absorption and fluorescence properties of molecular probes to the environment. Here, we present the current status of the multiscale strategy based on the combination of quantum chemistry with classical models. Potentials and limitations of the approach are discussed and an outlook on future developments is given with emphasis on the role that solvation dynamics can play. © 2015 Wiley Periodicals, Inc.

Computational enzyme design: Transitioning from catalytic proteins to enzymes

The widespread interest in enzymes stem from their ability to catalyze chemical reactions under mild and ecologically friendly conditions with unparalleled catalytic proficiencies. While thousands of naturally occurring enzymes have been identified and characterized, there are still numerous important applications for which there are no biological catalysts capable of performing the desired chemical transformation. In order to engineer enzymes for which there is no natural starting point, efforts using a combination of quantum chemistry and force-field based protein molecular modeling have led to the design of novel proteins capable of catalyzing chemical reactions not catalyzed by naturally occurring enzymes. Here we discuss the current status and potential avenues to pursue as the field of computational enzyme design moves forward.

Calculations of 31P Magnetic Shielding Constants of Derivatives of Betaine and Phosphine Molecules Dissolved in Different Solvents by Using Supermolecular Model and Combined Methods of Quantum Chemistry and Molecular Mechanics

Spatial structures of molecular clusters modeling a solvate shell around phosphorus-containing methyl- and butyl-derivatives of phosphine and betaine molecules dissolved in different solvents (acetone, toluene, formamide) have been calculated by using different variants of density functional theory (unrestricted Becke three-parameter Lee–Yang–Parr [UB3LYP], Perdew–Burke–Ernzerhof [PBE], optimized exchange functional [OPTX] developed by Handy and Cohen in conjunction with Lee–Yang–Parr [LYP] correlational functional [OLYP]) with 6-31G(d,p) and 6-31G++(d,p) basis sets. The 31P magnetic shielding constants for the structures are calculated with the usage of gauge-including atomic orbitals in UB3LYP/6-31G(d,p) and 6-31G++(d,p) methods. The modeling of molecular clusters is done by using the supermolecular model, the molecular mechanics method and the combination of quantum chemistry and molecular mechanics methods (QM/MM). The own N-layered integrated molecular orbital method (ONIOM) has been applied for modeling and calculating of isotropic 31P nucleus magnetic shielding of clusters of trimethylphosphine and trimethylbetaine molecules dissolved in acetone using combinations of UB3LYP/6-31G(d,p) (higher level) and unrestricted Hartree–Fock (UHF)/6-31G(d,p) (lower level) methods. Applicability of the ONIOM approach and different ways of modeling to the calculation of 31P nucleus magnetic shielding constants is studied. A comparison of the results obtained by the density functional theory, ONIOM and MM methods is given.

Light-Matter Interaction of Strong Laser Pulses in the Micro-, Nano-, and Pico-second Regimes

AbstractLight propagation in a medium is sensitively dependent on the shape and intensity of the optical pulse as well as on the electronic and vibrational structure of the basic molecular units. We review in this paper results of systematic studies of this problem for isotropic media. Our theoretical approach|the quantum mechanical{electrodynamical (QMED) approach is based on a quantum mechanical account of the many-level electron-nuclear medium coupled to a numerical solution of the density matrix and Maxwell's equations. This allows to accommodate a variety of nonlinear e ects which accomplish the propagation of strong light pulses. Particular attention is paid to the understanding of the role of coherent and sequential excitations of electron-nuclear degrees of freedom. The QMED combination of quantum chemistry with classical pulse propagation allows to estimate the optical transmission from cross sections of multi-photon absorption processes and from considerations of propagation e ects, saturation and pulse e ects. Results of the theory suggest that in the nonlinear regime it is often necessary to account simultaneously for coherent one-step and incoherent step-wise multi-photon absorption, as well as for o -resonant excitations even when resonance conditions prevail. The dynamic theory of nonlinear propagation of a few interacting intense light pulses is here highlighted in a study of the optical power limiting with platinum-organic molecular compounds.

Gas Diffusion and Solubility in Poly(organophosphazenes): Results of Molecular Simulation Studies

A combination of quantum chemistry, molecular dynamics, and Monte Carlo methods have been used to investigate gas diffusion and solubility in three isomeric poly[di(butoxyphosphazenes)] and in amorphous and crystalline states of poly[bis(2,2,2-trifluoroethoxyphosphazene)] (PTFEP). In this review of recently published studies reported from our laboratory, conclusions are reached in regards to the relationship between polymer structure and gas diffusion and sorption in poly(organophosphazenes). These conclusions also serve to validate our current understanding of the nature of gas transport in other polymers. Specifically, gas diffusivity has been shown to increase with increasing side-chain and main-chain mobility as determined from vectorial autocorrelation function analysis; however, high diffusivity is accompanied by a loss in diffusive selectivity resulting in decreasing permselectivity with increasing permeability. Simulation of crystalline supercells of PTFEP indicate that gas diffusion is unrestricted in the crystalline state as has been reported only for a few other polymers, principally poly(4-methyl-1-pentene). Gas solubility in poly(organophosphazenes) correlates well with gas condensability as measured by the Lennard–Jones potential well depth parameter, ɛ/k. Exceptions are cases where specific interactions can occur between gas molecules and the polymer chain such as is the case of CO2 and PTFEP. High-level ab initio calculations of the interaction of CO2 with low-molecular-weight fluoroalkanes indicate the presence of a weak quadrupole–dipole interaction. Association of CO2 with the trifluoromethyl groups of the trifluoroethoxy side chain of PTFEP has been confirmed by radial distribution function (RDF) analysis of MD trajectories. Comparison between solubility coefficients obtained from Grand Canonical Monte Carlo (GCMC) simulations of amorphous cells with experimental values of microcrystalline PTFEP indicates that gas solubility in polyphosphazenes such as PTFEP that exhibit a mesophase/crystalline state is greatly reduced.

MOLCAS: a program package for computational chemistry

The program system MOLCAS is a package for calculations of electronic and structural properties of molecular systems in gas, liquid, or solid phase. It contains a number of modern quantum chemical methods for studies of the electronic structure in ground and excited electronic states. A macromolecular environment can be modeled by a combination of quantum chemistry and molecular mechanics. It is further possible to describe a crystalline material using model potentials. Solvent effects can be treated using continuum models or by combining quantum chemical calculations with molecular dynamics or Monte-Carlo simulations. MOLCAS is especially adapted to treat systems with a complex electronic structure, where the simplest quantum chemical models do not work. These features together with the inclusion of relativistic effects makes it possible to treat with good accuracy systems including atoms from the entire periodic system. MOLCAS has effective methods for geometry optimization of equilibria, transition states, conical intersections, etc. This facilitates studies of excited state energy surfaces, spectroscopy, and photochemical processes.

Quantum refinement—a combination of quantum chemistry and protein crystallography

The combination of quantum mechanics and molecular mechanics (QM/MM) is one of the most promising approaches to study the structure, function, and properties of proteins. We here review our applications of QM/MM methods to alcohol dehydrogenase, blue copper proteins, iron–sulphur clusters, ferrochelatase, and myoglobin. We also describe our new quantum refinement method, which is a combination of quantum chemistry and protein crystallography. It has been shown to work properly and it can be used to improve the structure of protein metal centres in terms of the crystallographic R free factor and electron-density maps. It can be used to determine the protonation status of metal-bound solvent molecules in proteins by refining the various possible states and see which fits the crystallographic raw data best. Applications to ferrochelatase, cytochrome c 553, alcohol dehydrogenase, myoglobin, and methylmalonyl coenzyme A mutase are described.

Study of the Activity of Ga-ZSM-5 in the de-NOx Process by a Combination of Quantum Chemistry, Molecular Dynamics, and Computer Graphics Methods

Study of the Activity of Ga-ZSM-5 in the de-NOx Process by a Combination of Quantum Chemistry, Molecular Dynamics, and Computer Graphics Methods