A universal approach to solvation modeling

First and Second Reductions in an Aprotic Solvent: Comparing Computational and Experimental One-Electron Reduction Potentials for 345 Quinones

Using reference reduction potentials of quinones recently measured relative to the saturated calomel electrode (SCE) in N,N-dimethylformamide (DMF), we benchmark absolute one-electron reduction potentials computed for 345 Q/Q•- and 265 Q•-/Q2- half-reactions using adiabatic electron affinities computed with density functional theory and solvation energies computed with four continuum solvation models: IEF-PCM, C-PCM, COSMO, and SM12. Regression analyses indicate a strong linear correlation between experimental and absolute computed Q/Q•- reduction potentials with Pearson's correlation coefficient (r) between 0.95 and 0.96 and the mean absolute error (MAE) relative to the linear fit between 83.29 and 89.51 mV for different solvation methods when the slope of the regression is constrained to 1. The same analysis for Q•-/Q2- gave a linear regression with r between 0.74 and 0.90 and MAE between 95.87 and 144.53 mV, respectively. The y-intercept values obtained from the linear regressions are in good agreement with the range of absolute reduction potentials reported in the literature for the SCE but reveal several sources of systematic error. The y-intercepts from Q•-/Q2- calculations are lower than those from Q/Q•- by around 320-410 mV for IEF-PCM, C-PCM, and SM12 compared to 210 mV for COSMO. Systematic errors also arise between molecules having different ring sizes (benzoquinones, naphthoquinones, and anthraquinones) and different substituents (titratable vs nontitratable). SCF convergence issues were found to be a source of random error that was slightly reduced by directly optimizing the solute structure in the continuum solvent reaction field. While SM12 MAEs were lower than those of the other solvation models for Q/Q•-, SM12 had larger MAEs for Q•-/Q2- pointing to a larger error when describing multiply charged anions in DMF. Altogether, the results highlight the advantages of, and further need for, testing computational methods using a large experimental data set that is not skewed (e.g., having more titratable than nontitratable substituents on different parent groups or vice versa) to help further distinguish between sources of random and systematic errors in the calculations.

PW-SMD: A Plane-Wave Implicit Solvation Model Based on Electron Density for Surface Chemistry and Crystalline Systems in Aqueous Solution

Electron density-based implicit solvation models are a class of techniques for quantifying solvation effects and calculating free energies of solvation without an explicit representation of solvent molecules. Integral to the accuracy of solvation modeling is the proper definition of the solvation shell separating the solute molecule from the solvent environment, allowing for a physical partitioning of the free energies of solvation. Unlike state-of-the-art implicit solvation models for molecular quantum chemistry calculations, e.g., the solvation model based on solute electron density (SMD), solvation models for systems under periodic boundary conditions with plane-wave (PW) basis sets have been limited in their accuracy. Furthermore, a unified implicit solvation model with both homogeneous solution-phase and heterogeneous interfacial structures treated on equal footing is needed. In order to address this challenge, we developed a high-accuracy solvation model for periodic PW calculations that is applicable to molecular, ionic, interfacial, and bulk-phase chemistry. Our model, PW-SMD, is an extension of the SMD molecular solvation model to periodic systems in water. The free energy of solvation is partitioned into the electrostatic and cavity-dispersion-solvent structure (CDS) contributions. The electrostatic contributions of the solvation shell surrounding solute structures are parametrized based on their geometric and physical properties. In addition, the nonelectrostatic contribution to the solvation energy is accounted for by extending the CDS formalism of SMD to incorporate periodic boundary conditions. We validate the accuracy and robustness of our solvation model by comparing predicted solvation free energies against experimental data for molecular and ionic systems, carved-cluster composite energetic models of solvated reaction energies and barriers on surface systems, and deep-learning-accelerated ab initio molecular dynamics (AIMD). Our developed periodic implicit solvation model shows significantly improved accuracy compared to previous work (namely, solvation models in aqueous solution) and can be applied to simulate solvent effects in a wide range of surface and crystalline materials.

Quantum cluster equilibrium theory applied to liquid ammonia

Through this paper, the authors propose using the quantum cluster equilibrium (QCE) theory to reinvestigate ammonia clusters in the liquid phase. The ammonia clusters from size monomer to hexadecamer were considered to simulate the liquid ammonia in this approach. The clusterset used to model the liquid ammonia is an ensemble of different structures of ammonia clusters. After studious research of the representative configurations of ammonia clusters through the cluster research program ABCluster, the configurations have been optimized at the MN15/6-31++G(d,p) level of theory. These optimizations lead to geometries and frequencies as inputs for the Peacemaker code. The QCE study of this molecular system permits us to get the liquid phase populations in a temperature range of 190-260 K, covering the temperatures from the melting point to the boiling point. The results show that the population of liquid ammonia comprises mainly the ammonia hexadecamer followed by pentadecamer, tetradecamer, and tridecamer. We noted that the small-sized ammonia clusters do not contribute to the population of liquid ammonia. In addition, the thermodynamic properties, such as heat of vaporization, heat capacity, entropy, enthalpy, and free energies, obtained by the QCE theory have been compared to the experiment given some relatively good agreements in the gas phase and show considerable discrepancies in liquid phase except the density. Finally, based on the predicted population, we calculated the infrared spectrum of liquid ammonia at 215 K temperature. It comes out that the calculated infrared spectrum qualitatively agrees with the experiment.

Soft-sphere continuum solvation models for nonaqueous solvents

Solvation effects profoundly influence the characteristics and behavior of chemical systems in liquid solutions. The interaction between solute and solvent molecules intricately impacts solubility, reactivity, stability, and various chemical processes. Continuum solvation models gained prominence in quantum chemistry by implicitly capturing these interactions and enabling efficient investigations of diverse chemical systems in solution. In comparison, continuum solvation models in condensed matter simulation are very recent. Among these, the self-consistent continuum solvation (SCCS) and the soft-sphere continuum solvation models (SSCS) have been among the first to be successfully parameterized and extended to model periodic systems in aqueous solutions and electrolytes. As most continuum approaches, these models depend on a number of parameters that are linked to experimental or theoretical properties of the solvent, or that can be tuned based on reference data. Here, we present a systematic parameterization of the SSCS model for over 100 nonaqueous solvents. We validate the model's efficacy across diverse solvent environments by leveraging experimental solvation-free energies and partition coefficients from comprehensive databases. The average root means square error over all the solvents was calculated as 0.85 kcal/mol which is below the chemical accuracy (1 kcal/mol). Similarly to what has been reported by Hille et al. (J. Chem. Phys. 2019, 150, 041710.) for the SCCS model, a single-parameter model accurately reproduces experimental solvation energies, showcasing the transferability and predictive power of these continuum approaches. Our findings underscore the potential for a unified approach to predict solvation properties, paving the way for enhanced computational studies across various chemical environments.

Consistent Analytical Second Derivatives of the Kohn-Sham DFT Energy in the Framework of the Conductor-Like Screening Model through Gaussian Charge Distributions

The use of implicit solvation models such as the conductor-like screening model (COSMO) in quantum chemical calculations is very common, as both a rough estimate of solvation effects as well as a general tool for stabilizing ionic molecular structures. In order to generate a smooth potential energy surface as well as consistent gradients, it is necessary to apply the Gaussian charge model (GCM) for the COSMO charges. This work introduces an efficient implementation for consistent analytical second derivatives of the electronic energy with COSMO-GCM in the framework of the Kohn-Sham density functional theory. This is used to investigate the infrared spectroscopy of amino acids in aqueous solution, where the impact of pH on the molecular structure and vibrational spectra is examined. Furthermore, the structure and stability of selected all-metal aromatic cluster ions are assessed.

Explainable Supervised Machine Learning Model To Predict Solvation Gibbs Energy

Many challenges persist in developing accurate computational models for predicting solvation free energy (ΔGsol). Despite recent developments in Machine Learning (ML) methodologies that outperformed traditional quantum mechanical models, several issues remain concerning explanatory insights for broad chemical predictions with an acceptable speed-accuracy trade-off. To overcome this, we present a novel supervised ML model to predict the ΔGsol for an array of solvent-solute pairs. Using two different ensemble regressor algorithms, we made fast and accurate property predictions using open-source chemical features, encoding complex electronic, structural, and surface area descriptors for every solvent and solute. By integrating molecular properties and chemical interaction features, we have analyzed individual descriptor importance and optimized our model though explanatory information form feature groups. On aqueous and organic solvent databases, ML models revealed the predictive relevance of solutes with increasing polar surface area and decreasing polarizability, yielding better results than state-of-the-art benchmark Neural Network methods (without complex quantum mechanical or molecular dynamic simulations). Both algorithms successfully outperformed previous ΔGsol predictions methods, with a maximum absolute error of 0.22 ± 0.02 kcal mol-1, further validated in an external benchmark database and with solvent hold-out tests. With these explanatory and statistical insights, they allow a thoughtful application of this method for predicting other thermodynamic properties, stressing the relevance of ML modeling for further complex computational chemistry problems.

Unraveling Water Solvation Effects with Quantum Mechanics/Molecular Mechanics Semiclassical Vibrational Spectroscopy: The Case of Thymidine

We introduce a quantum mechanics/molecular mechanics semiclassical method for studying the solvation process of molecules in water at the nuclear quantum mechanical level with atomistic detail. We employ it in vibrational spectroscopy calculations because this is a tool that is very sensitive to the molecular environment. Specifically, we look at the vibrational spectroscopy of thymidine in liquid water. We find that the C═O frequency red shift and the C═C frequency blue shift, experienced by thymidyne upon solvation, are mainly due to reciprocal polarization effects, that the molecule and the water solvent exert on each other, and nuclear zero-point energy effects. In general, this work provides an accurate and practical tool to study quantum vibrational spectroscopy in solution and condensed phase, incorporating high-level and computationally affordable descriptions of both electronic and nuclear problems.

Docking for EP4R antagonists active against inflammatory pain

The lipid prostaglandin E2 (PGE2) mediates inflammatory pain by activating G protein-coupled receptors, including the prostaglandin E2 receptor 4 (EP4R). Nonsteroidal anti-inflammatory drugs (NSAIDs) reduce nociception by inhibiting prostaglandin synthesis, however, the disruption of upstream prostanoid biosynthesis can lead to pleiotropic effects including gastrointestinal bleeding and cardiac complications. In contrast, by acting downstream, EP4R antagonists may act specifically as anti-inflammatory agents and, to date, no selective EP4R antagonists have been approved for human use. In this work, seeking to diversify EP4R antagonist scaffolds, we computationally dock over 400 million compounds against an EP4R crystal structure and experimentally validate 71 highly ranked, de novo synthesized molecules. Further, we show how structure-based optimization of initial docking hits identifies a potent and selective antagonist with 16 nanomolar potency. Finally, we demonstrate favorable pharmacokinetics for the discovered compound as well as anti-allodynic and anti-inflammatory activity in several preclinical pain models in mice.

ConfSolv: Prediction of Solute Conformer-Free Energies across a Range of Solvents

Predicting Gibbs free energy of solution is key to understanding the solvent effects on thermodynamics and reaction rates for kinetic modeling. Accurately computing solution free energies requires the enumeration and evaluation of relevant solute conformers in solution. However, even after generation of relevant conformers, determining their free energy of solution requires an expensive workflow consisting of several ab initio computational chemistry calculations. To help address this challenge, we generate a large data set of solution free energies for nearly 44,000 solutes with almost 9 million conformers calculated in 41 different solvents using density functional theory and COSMO-RS and quantify the impact of solute conformers on the solution free energy. We then train a message passing neural network to predict the relative solution free energies of a set of solute conformers, enabling the identification of a small subset of thermodynamically relevant conformers. The model offers substantial computational time savings with predictions usually substantially within 1 kcal/mol of the free energy of the solution calculated by using computational chemical methods.

Predicting Solvation Free Energies Using Electronegativity-Equalization Atomic Charges and a Dense Neural Network: A Generalized-Born Approach

I propose a dense Neural Network, ESE-GB-DNN, for evaluation of solvation free energies ΔG°solv for molecules and ions in water and nonaqueous solvents. As input features, it employs generalized-Born monatomic and diatomic terms, as well as atomic surface areas and the molecular volume. The electrostatics calculation is based on a specially modified version of electronegativity-equalization atomic charges. ESE-GB-DNN evaluates ΔG°solv in a simple and highly efficient way, yet it offers a high accuracy, often challenging that of standard DFT-based methods. For neutral solutes, ESE-GB-DNN yields an RMSE between 0.7 and 1.3 kcal/mol, depending on the solvent class. ESE-GB-DNN performs particularly well for nonaqueous solutions of ions, with an RMSE of about 0.7 kcal/mol. For ions in water, the RMSE is larger (2.9 kcal/mol).

Dense Neural Network for Calculating Solvation Free Energies from Electronegativity-Equalization Atomic Charges

I propose a dense Neural Network for evaluation of solvation free energies ΔG°solv for molecules and ions in water and nonaqueous solvents, Easy Solvation Energy with Electronegativity Equalization charges and Dense Neural Network (ESE-EE-DNN). As input features, it uses the Conductor-like Screening Model (COSMO) electrostatic energy, atomic cavity surface areas, total cavity volume, and induced surface charges. For the COSMO calculation, electronegativity-equalization atomic charges are employed. ESE-EE-DNN exhibits fairly high accuracy, similar or even superior to that of mainstream density functional theory-based methods. For neutral solutes in water, polar protic, polar aprotic, and nonpolar solvents, ESE-EE-DNN yields a root-mean-square error (RMSE) of 1.25, 1.36, 0.70, and 0.71 kcal/mol, respectively. ESE-EE-DNN is particularly advantageous for ionic solutes, with an RMSE of 2.82 and 1.42 kcal/mol for aqueous and nonaqueous ion solutions, correspondingly. ESE-EE-DNN is very efficient due to a fast evaluation of the electronegativity-equalization charges.

Isolated and solvated chiroptical behavior in conformationally flexible butanamines

The nonresonant optical activity of two highly flexible aliphatic amines, (2R)-3-methyl-2-butanamine (R-MBA) and (2R)-(3,3)-dimethyl-2-butanamine (R-DMBA), has been probed under isolated and solvated conditions to examine the roles of conformational isomerism and to explore the influence of extrinsic perturbations. The optical rotatory dispersion (ORD) measured in six solvents presented uniformly negative rotatory powers over the 320-590 nm region, with the long-wavelength magnitude of chiroptical response growing nearly monotonically as the dielectric constant of the surroundings diminished. The intrinsic specific optical rotation,α λ T (in deg dm-1 [g/mL]-1 ), extracted for ambient vapor-phase samples of R-MBA [-11.031(98) and -2.29 (11)] and R-DMBA [-9.434 (72) and -1.350 (48)] at 355 and 633 nm were best reproduced by counterintuitive solvents of high polarity (yet low polarizability) like acetonitrile and methanol. Attempts to interpret observed spectral signatures quantitatively relied on the linear-response frameworks of density-functional theory (B3LYP, cam-B3LYP, and dispersion-corrected analogs) and coupled-cluster theory (CCSD), with variants of the polarizable continuum model (PCM) deployed to account for the effects of implicit solvation. Building on the identification of several low-lying equilibrium geometries (nine for R-MBA and three for R-DMBA), ensemble-averaged ORD profiles were calculated at T = 300 K by means of the independent-conformer ansatz, which enabled response properties predicted for the optimized structure of each isomer to be combined through Boltzmann-weighted population fractions derived from corresponding relative internal-energy or free-energy values, the latter of which stemmed from composite CBS-APNO and G4 analyses. Although reasonable accord between theory and experiment was realized for the isolated (vapor-phase) species, the solution-phase results were less satisfactory and tended to degrade progressively as the solvent polarity increased. These trends were attributed to solvent-mediated changes in structural parameters and energy metrics for the transition states that separate and putatively isolate the equilibrium conformations supported by the ground electronic potential-energy surface, with the resulting displacement of barrier locations and/or decrease of barrier heights compromising the underlying premise of the independent-conformer ansatz.

Prediction of toluene/water partition coefficients in the SAMPL9 blind challenge: assessment of machine learning and IEF-PCM/MST continuum solvation models

In recent years the use of partition systems other than the widely used biphasic n-octanol/water has received increased attention to gain insight into the molecular features that dictate the lipophilicity of compounds. Thus, the difference between n-octanol/water and toluene/water partition coefficients has proven to be a valuable descriptor to study the propensity of molecules to form intramolecular hydrogen bonds and exhibit chameleon-like properties that modulate solubility and permeability. In this context, this study reports the experimental toluene/water partition coefficients (log P_tol/w) for a series of 16 drugs that were selected as an external test set in the framework of the Statistical Assessment of the Modeling of Proteins and Ligands (SAMPL) blind challenge. This external set has been used by the computational community to calibrate their methods in the current edition (SAMPL9) of this contest. Furthermore, the study also investigates the performance of two computational strategies for the prediction of log P_tol/w. The first relies on the development of two machine learning (ML) models, which are built up by combining the selection of 11 molecular descriptors in conjunction with either the multiple linear regression (MLR) or the random forest regression (RFR) model to target a dataset of 252 experimental log P_tol/w values. The second consists of the parametrization of the IEF-PCM/MST continuum solvation model from B3LYP/6-31G(d) calculations to predict the solvation free energies of 163 compounds in toluene and benzene. The performance of the ML and IEF-PCM/MST models has been calibrated against external test sets, including the compounds that define the SAMPL9 log P_tol/w challenge. The results are used to discuss the merits and weaknesses of the two computational approaches.

Hydration effects on the vibrational properties of carboxylates: From continuum models to QM/MM simulations

The presence of carboxyl groups in a molecule delivers an affinity to metal cations and a sensitivity to the chemical environment, especially for an environment that can give rise to intermolecular hydrogen bonds. Carboxylate groups can also induce intramolecular interactions, such as the formation of hydrogen bonds with donor groups, leading to an impact on the conformational space of biomolecules. In the latter case, the protonation state of the amino groups plays an important role. In order to provide an accurate description of the modifications induced in a carboxylated molecule by the formation of hydrogen bonds, one needs a compromise between a quantum chemical description of the system and the necessity to take into account explicit solvent molecules. In this work, we propose a bottom-up approach to study the conformational space and the carboxylate stretching band of (bio)organic anions. Starting from the anions in a continuum solvent, we then move to calculations using a microsolvation approach including one explicit water molecule per polar group, immersed in a continuum. Finally, we run QM/MM molecular dynamics simulations to analyze the solvation properties and to explore the anions conformational space. The results thus obtained are in good agreement with the description given by the microsolvation approach and they bring a more detailed description of the solvation shell and of the intermolecular hydrogen bonds.

Study on ESIPT of salicylaldehyde derivative EQCN in DCM solvent

The fluorescence of salicylaldehyde derivative (EQCN) as an excitation-wavelength-dependent molecule with long-persistent luminescence has been investigated experimentally and theoretically. However, the excited-state intramolecular proton transfer (ESIPT) process mechanism and optical property associated with photochemical process of EQCN molecule in dichloromethane (DCM) solvent has not been discussed in detail. In this work, density functional theory (DFT) and time-dependent density functional theory (TD-DFT) were used to investigate ESIPT process of EQCN molecule in DCM solvent. By optimizing the geometry of the EQCN molecule, the hydrogen bond interaction of EQCN molecule of Enol structure in excited state (S₁ state) is strengthened. The calculated absorption peak and fluorescence peak agree well with the experimental values. Based on the optimized geometric structure, the frontier molecular orbital isosurface (FMOs) were drawn, and the redistribution of electron density in DCM solvent was depicted, which intuitively explain the changes in the photophysical properties of EQCN. Through the calculated potential energy curves (PECs) of EQCN in both DCM solvent and ethanol solvent, the ESIPT process of EQCN was found more likely to occur in ethanol solvents.

Screening and Biological Evaluation of Soluble Epoxide Hydrolase Inhibitors: Assessing the Role of Hydrophobicity in the Pharmacophore-Guided Search of Novel Hits

The human soluble epoxide hydrolase (sEH) is a bifunctional enzyme that modulates the levels of regulatory epoxy lipids. The hydrolase activity is carried out by a catalytic triad located at the center of a wide L-shaped binding site, which contains two hydrophobic subpockets at both sides. On the basis of these structural features, it can be assumed that desolvation is a major factor in determining the maximal achievable affinity that can be attained for this pocket. Accordingly, hydrophobic descriptors may be better suited to the search of novel hits targeting this enzyme. This study examines the suitability of quantum mechanically derived hydrophobic descriptors in the discovery of novel sEH inhibitors. To this end, three-dimensional quantitative structure-activity relationship (3D-QSAR) pharmacophores were generated by combining electrostatic and steric or alternatively hydrophobic and hydrogen-bond parameters in conjunction with a tailored list of 76 known sEH inhibitors. The pharmacophore models were then validated by using two external sets chosen (i) to rank the potency of four distinct series of compounds and (ii) to discriminate actives from decoys, using in both cases datasets taken from the literature. Finally, a prospective study was performed including a virtual screening of two chemical libraries to identify new potential hits, which were subsequently experimentally tested for their inhibitory activity on human, rat, and mouse sEH. The use of hydrophobic-based descriptors led to the identification of six compounds as inhibitors of the human enzyme with IC50 < 20 nM, including two with IC50 values of 0.4 and 0.7 nM. The results support the use of hydrophobic descriptors as a valuable tool in the search of novel scaffolds that encode a proper hydrophilic/hydrophobic distribution complementary to the target's binding site.

The entropic penalty for associative reactions and their physical treatment during routine computations

A systematic study of the entropic penalty for associative reactions is presented. It is shown that computed solution-phase Gibbs free energies typically overestimate entropic contributions. This entropic penalty for associative reactions in solution, i.e., if the number of particles decreases along the reaction coordinate (sum of stoichiometric numbers ), originates from the insufficient treatment of entropic effects by implicit solvent models. We propose an additive correction scheme to Gibbs free energies that is suitable for routine applications by non-expert users. This correction is based on Garza's formalism for the solution-phase entropy [A. J. Garza, J. Chem. Theory Comput., 2019, 15, 3204.] that is physically sound and embedded into an efficient black-box type algorithm. To critically evaluate the entropic penalty and its proposed treatment, we compiled an experimental benchmark set of 31 Δ_rG and 22 in 15 different solvents. Using a representative best-practice computational protocol (at wave function theory (WFT) based DLPNO-CCSD(T) and density functional theory (DFT) based revDSD-PBEP86-D4 level with an implicit solvent model), we determined a sizeable entropic penalty ranging from 2-11 kcal mol^-1. Using the correction scheme presented herein, the entropic penalty is corrected to the chemical accuracy of ≤1 kcal mol^-1 (WFT and DFT). The same applies to at the WFT level. Barriers at the DFT level are overestimated by 2 kcal mol^-1 (classic) and underestimated by 2 kcal mol^-1 (corrected). This effect is attributed to the finding that barriers computed at the DFT level are systematically 2-3 kcal mol^-1 lower than barriers obtained with WFT.

Insights from computational analysis: Excited-state hydrogen-bonding interactions and ESIPT processes in phenothiazine derivatives

Organic materials with Mechanofluorochromism (MFC) properties have potential application value. Phenothiazine derivatives are a class of substances with MFC properties that have been synthesized and reported in experiments (Dyes and Pigments 172 (2020) 107835). Dual fluorescence of a series of phenothiazine derivatives is observed in the experiment, which proved that the ESIPT process is carried out. In this work, we choose phenothiazine derivatives (C2PAHN, C4PAHN, C8PAHN) as models to theoretically analyze the influence of different alkyl chain lengths on the excited state intramolecular proton transfer (ESIPT). In addition, the shift value of fluorescence spectrum is related to the length of alkyl chain. The fluorescence shift of C2PAHN is the largest (6.31 nm), and that of C8PAHN is the smallest (2.40 nm). The theory of density functional theory (DFT) and time-dependent density functional theory (TD-DFT) are adopted to simulate the molecular dynamics in the ground state and excited state. The analysis of the optimized molecular geometry parameters and infrared vibrational spectroscopy (IR) illustrate the stronger hydrogen bonding of the excited state molecules, which is favorable for the progress of ESIPT. Fluorescence spectroscopy reveals that the appropriate increase or decrease of alkyl chains would change the photophysical properties of the molecules. Frontier molecular orbitals (FMOs) indicate that the rearrangement of electron density from electronic level to is the driving force of the ESIPT process. Reduction density gradient (RDG) surfaces and Natural Population Analysis (NPA) tentatively lead to the conclusion that alkyl chain length is inversely proportional to hydrogen bond strength. Finally, the data are qualitatively analyzed by scanning the potential energy curves, and it is concluded that the longer the alkyl chain, the weaker the hydrogen bonding effect and the more unfavorable the ESIPT process.

State averaged CASSCF in AMOEBA polarizable water model for simulating nonadiabatic molecular dynamics with nonequilibrium solvation effects

This paper presents a state-averaged complete active space self-consistent field (SA-CASSCF) in the atomic multipole optimized energetics for biomolecular application (AMOEBA) polarizable water model, which enables rigorous simulation of non-adiabatic molecular dynamics with nonequilibrium solvation effects. The molecular orbital and configuration interaction coefficients of the solute wavefunction, and the induced dipoles on solvent atoms, are solved by minimizing the state averaged energy variationally. In particular, by formulating AMOEBA water models and the polarizable continuum model (PCM) in a unified way, the algorithms developed for computing SA-CASSCF/PCM energies, analytical gradients, and non-adiabatic couplings in our previous work can be generalized to SA-CASSCF/AMOEBA by properly substituting a specific list of variables. Implementation of this method will be discussed with the emphasis on how the calculations of different terms are partitioned between the quantum chemistry and molecular mechanics codes. We will present and discuss results that demonstrate the accuracy and performance of the implementation. Next, we will discuss results that compare three solvent models that work with SA-CASSCF, i.e., PCM, fixed-charge force fields, and the newly implemented AMOEBA. Finally, the new SA-CASSCF/AMOEBA method has been interfaced with the ab initio multiple spawning method to carry out non-adiabatic molecular dynamics simulations. This method is demonstrated by simulating the photodynamics of the model retinal protonated Schiff base molecule in water.

Modelling the octanol-air partition coefficient of aromatic pollutants based on the solvation free energy and the dimer effect

In this study, generalized predictive models were developed to estimate K_OA of four kinds of aromatic pollutants based on the calculated solvation free energy and taking the dimer effect into account. Uncorrected log K_OA values, which were directly estimated from the calculated solvation free energy of individual molecules, underestimated experimental values, and the deviation increased with increasing log K_OA. Dimers were found to greatly affect the apparent K_OA values of these aromatic pollutants, which were driven by π-π interactions. London dispersion and exchange-repulsion terms were identified to be dominant components of the underlying π-π interactions. It is interesting to find that the π-π interactions of polybrominated diphenyl ethers correlate with not only the molecular polarizability but also the size of opposing aromatic surfaces, which leads to a different trend of π-π interactions from other aromatic pollutants. A universal quantitative structure-activity relationship model was developed to estimate the proportion of dimers based on five molecular structural descriptors relevant to the π-π interactions. After calibration with the dimer effect, estimations of log K_OA were consistent with experimental values. Therefore, the dimer effect should be taken into consideration when investigating the partition behavior of aromatic pollutants, and the solvation free energy model could be an alternative method for the prediction of K_OA.

Computer simulation of molecular recognition in biomolecular system: from in silico screening to generalized ensembles

Prediction of ligand-receptor complex structure is important in both the basic science and the industry such as drug discovery. We report various computation molecular docking methods: fundamental in silico (virtual) screening, ensemble docking, enhanced sampling (generalized ensemble) methods, and other methods to improve the accuracy of the complex structure. We explain not only the merits of these methods but also their limits of application and discuss some interaction terms which are not considered in the in silico methods. In silico screening and ensemble docking are useful when one focuses on obtaining the native complex structure (the most thermodynamically stable complex). Generalized ensemble method provides a free-energy landscape, which shows the distribution of the most stable complex structure and semi-stable ones in a conformational space. Also, barriers separating those stable structures are identified. A researcher should select one of the methods according to the research aim and depending on complexity of the molecular system to be studied.

Ultrafast Dynamics of Solute Molecules Probed by Resonant Optical Kerr Effect Spectroscopy

Ultrafast molecular dynamics in fluids is of great importance in many biological and chemical systems. Although such dynamics in bulk liquids has been explored by various methods, experimental tools that unveil the dynamics of solvated solutes are limited. In this work, we have developed resonant optical Kerr effect spectroscopy (ROKE), which is an analogue of optical Kerr effect spectroscopy that measures the reorientational relaxation of a dilute solute in solution. By adjusting the pump and probe wavelengths at the resonant absorption band of a solute, the time response of the solute was distinguished easily from the negligible signal of the solvent. The heterodyne detection of ROKE enables the determination of reorientational relaxation time constants with an accuracy of 2.6%. The signal-to-noise ratio was high enough (average ∼26.7) to obtain an adequate signal from even a 10 μM solution. Thus, ROKE is a powerful tool to study solute dynamics with high sensitivity in a broad range of applications.

Why Ortho- and Para-Hydroxy Metabolites Can Scavenge Free Radicals That the Parent Atorvastatin Cannot? Important Pharmacologic Insight from Quantum Chemistry

The pharmaceutical success of atorvastatin (ATV), a widely employed drug against the "bad" cholesterol (LDL) and cardiovascular diseases, traces back to its ability to scavenge free radicals. Unfortunately, information on its antioxidant properties is missing or unreliable. Here, we report detailed quantum chemical results for ATV and its ortho- and para-hydroxy metabolites (o-ATV, p-ATV) in the methanolic phase. They comprise global reactivity indices, bond order indices, and spin densities as well as all relevant enthalpies of reaction (bond dissociation BDE, ionization IP and electron attachment EA, proton detachment PDE and proton affinity PA, and electron transfer ETE). With these properties in hand, we can provide the first theoretical explanation of the experimental finding that, due to their free radical scavenging activity, ATV hydroxy metabolites rather than the parent ATV, have substantial inhibitory effect on LDL and the like. Surprisingly (because it is contrary to the most cases currently known), we unambiguously found that HAT (direct hydrogen atom transfer) rather than SPLET (sequential proton loss electron transfer) or SET-PT (stepwise electron transfer proton transfer) is the thermodynamically preferred pathway by which o-ATV and p-ATV in methanolic phase can scavenge DPPH• (1,1-diphenyl-2-picrylhydrazyl) radicals. From a quantum chemical perspective, the ATV's species investigated are surprising because of the nontrivial correlations between bond dissociation energies, bond lengths, bond order indices and pertaining stretching frequencies, which do not fit the framework of naive chemical intuition.

A Closed-Form, Analytical Approximation for Apparent Surface Charge and Electric Field of Molecules

Closed-form, analytical approximations for electrostatic properties of molecules are of unique value as these can provide computational speed, versatility, and physical insight. Here, we have derived a simple, closed-form formula for the apparent surface charge (ASC) as well as for the electric field generated by a molecular charge distribution in aqueous solution. The approximation, with no fitted parameters, was tested against numerical solutions of the Poisson equation, where it has produced a significant speed-up. For neutral small molecules, the hydration free energies estimated from the closed-form ASC formula are within 0.8 kcal/mol RMSD from the numerical Poisson reference; the electric field at the surface is in quantitative agreement with the reference. Performance of the approximation was also tested on larger structures, including a protein, a DNA fragment, and a viral receptor-target complex. For all structures tested, a near-quantitative agreement with the numerical Poisson reference was achieved, except in regions of high negative curvature, where the new approximation is still qualitatively correct. A unique efficiency feature of the proposed "source-based″ closed-form approximation is that the ASC and electric field can be estimated individually at any point or surface patch, without the need to obtain the full global solution. An open-source software implementation of the method is available: https://people.cs.vt.edu/~onufriev/CODES/aasc.zip.

Towards a transferable nonelectrostatic model for continuum solvation: The electrostatic and nonelectrostatic energy correction model

In this work, we introduce an electrostatic and non-electrostatic (ENE) correction to the solvation energy based on the Solvent-Accessible Surface Area (SASA) of the solute and the solvent static dielectric constant. The proposed correction was developed for neutral solutes in non-aqueous solvents, considering three different implicit solvation models based on a Self-Consistent Reaction Field treatment of solute-solvent mutual polarization using an Apparent Surface Charge formalism, namely the Integral Equation Formalism of the Polarizable Continuum Model using a continuous surface charge scheme (PCM), the Solvation Model based on solute electron density (SMD), and the generalized Finite-Difference Poisson-Boltzmann (FDPB) model. The proposed correction was parametrized on a diverse training set of 4980 solvation data from the Solv@tum database of experimental solvation energies, and validated on the non-aqueous subset of the MNSOL database comprising 2140 solvation energies. The performances of the proposed ENE models with minimal and extended parameters formulations have been analyzed and the latter variant has been further compared to the widely used Cavity, Dispersion, and Solvent structural effects (CDS) non-electrostatic model originally developed for the SMx family of implicit solvation models. Overall, a very good agreement between the computed solvation energies with the ENE correction and the reference experimental data has been found on both the training and test sets for all continuum solvation models considered. Furthermore, results for the ENE correction are on par with the reference CDS non-electrostatic model for both SMD and FDPB electrostatics, but with the advantage of using a lower number of parameters and thus an improved transferability between different electrostatics treatments.

Octanol-Water Partition Coefficients of Fluorinated Drug Molecules with Continuum Solvation Models

In this work, we have examined the efficiency of continuum solvation models, used with density functional theory methods, in calculating octanol-water partition coefficients (logP) of 56 fluorine containing drug molecules. The solvation model based on density model computed logP values that are in good agreement with the benchmark values. The conductor-like polarizable continuum models computed results have issues in predicting correct trend, often with reversal of sign from benchmark. The choice of basis set does not show significant effect, and the selection of atomic radii affects geometry convergence during calculations.

Predicting Solubility Limits of Organic Solutes for a Wide Range of Solvents and Temperatures

The solubility of organic molecules is crucial in organic synthesis and industrial chemistry; it is important in the design of many phase separation and purification units, and it controls the migration of many species into the environment. To decide which solvents and temperatures can be used in the design of new processes, trial and error is often used, as the choice is restricted by unknown solid solubility limits. Here, we present a fast and convenient computational method for estimating the solubility of solid neutral organic molecules in water and many organic solvents for a broad range of temperatures. The model is developed by combining fundamental thermodynamic equations with machine learning models for solvation free energy, solvation enthalpy, Abraham solute parameters, and aqueous solid solubility at 298 K. We provide free open-source and online tools for the prediction of solid solubility limits and a curated data collection (SolProp) that includes more than 5000 experimental solid solubility values for validation of the model. The model predictions are accurate for aqueous systems and for a huge range of organic solvents up to 550 K or higher. Methods to further improve solid solubility predictions by providing experimental data on the solute of interest in another solvent, or on the solute's sublimation enthalpy, are also presented.

State-specific solvation for restricted active space spin-flip (RAS-SF) wave functions based on the polarizable continuum formalism

The restricted active space spin-flip (RAS-SF) formalism is a particular form of single-reference configuration interaction that can describe some forms of strong correlation at a relatively low cost and which has recently been formulated for the description of charge-transfer excited states. Here, we introduce both equilibrium and nonequilibrium versions of a state-specific solvation correction for vertical transition energies computed using RAS-SF wave functions, based on the framework of a polarizable continuum model (PCM). Ground-state polarization is described using the solvent's static dielectric constant and in the nonequilibrium solvation approach that polarization is modified upon vertical excitation using the solvent's optical dielectric constant. Benchmark calculations are reported for well-studied models of photo-induced charge transfer, including naphthalene dimer, C₂H₄⋯C₂F₄, pentacene dimer, and perylene diimide (PDI) dimer, several of which are important in organic photovoltaic applications. For the PDI dimer, we demonstrate that the charge-transfer character of the excited states is enhanced in the presence of a low-dielectric medium (static dielectric constant ɛ₀ = 3) as compared to a gas-phase calculation (ɛ₀ = 1). This stabilizes mechanistic traps for singlet fission and helps to explain experimental singlet fission rates. We also examine the effects of nonequilibrium solvation on charge-separated states in an intramolecular singlet fission chromophore, where we demonstrate that the energetic ordering of the states changes as a function of solvent polarity. The RAS-SF + PCM methodology that is reported here provides a framework to study charge-separated states in solution and in photovoltaic materials.

Electrocatalysis in Alkaline Media and Alkaline Membrane-Based Energy Technologies

Hydrogen energy-based electrochemical energy conversion technologies offer the promise of enabling a transition of the global energy landscape from fossil fuels to renewable energy. Here, we present a comprehensive review of the fundamentals of electrocatalysis in alkaline media and applications in alkaline-based energy technologies, particularly alkaline fuel cells and water electrolyzers. Anion exchange (alkaline) membrane fuel cells (AEMFCs) enable the use of nonprecious electrocatalysts for the sluggish oxygen reduction reaction (ORR), relative to proton exchange membrane fuel cells (PEMFCs), which require Pt-based electrocatalysts. However, the hydrogen oxidation reaction (HOR) kinetics is significantly slower in alkaline media than in acidic media. Understanding these phenomena requires applying theoretical and experimental methods to unravel molecular-level thermodynamics and kinetics of hydrogen and oxygen electrocatalysis and, particularly, the proton-coupled electron transfer (PCET) process that takes place in a proton-deficient alkaline media. Extensive electrochemical and spectroscopic studies, on single-crystal Pt and metal oxides, have contributed to the development of activity descriptors, as well as the identification of the nature of active sites, and the rate-determining steps of the HOR and ORR. Among these, the structure and reactivity of interfacial water serve as key potential and pH-dependent kinetic factors that are helping elucidate the origins of the HOR and ORR activity differences in acids and bases. Additionally, deliberately modulating and controlling catalyst-support interactions have provided valuable insights for enhancing catalyst accessibility and durability during operation. The design and synthesis of highly conductive and durable alkaline membranes/ionomers have enabled AEMFCs to reach initial performance metrics equal to or higher than those of PEMFCs. We emphasize the importance of using membrane electrode assemblies (MEAs) to integrate the often separately pursued/optimized electrocatalyst/support and membranes/ionomer components. Operando/in situ methods, at multiscales, and ab initio simulations provide a mechanistic understanding of electron, ion, and mass transport at catalyst/ionomer/membrane interfaces and the necessary guidance to achieve fuel cell operation in air over thousands of hours. We hope that this Review will serve as a roadmap for advancing the scientific understanding of the fundamental factors governing electrochemical energy conversion in alkaline media with the ultimate goal of achieving ultralow Pt or precious-metal-free high-performance and durable alkaline fuel cells and related technologies.

Electrochemical analysis in charge-transfer science: The devil in the details

It is easy to carry out electrochemical analysis. It is demanding, however, to do it right, as inherent challenges, emerging from details in the data collection and the result interpretation, frequently present themselves. In pertinence to electron-donor-acceptor interactions, herein, we focus on voltammetrically obtained electrochemical potentials and their immense utility for extracting important characteristics of molecular analytes. Recommendations how to address key pending challenges, based on recent developments in electroanalysis and charge-transfer science, accompany the discussions on undesired impacts from irreversibility of oxidation and reduction, supporting electrolytes, choices of reference, liquid junctions, and 'nonideality' of molecular shapes. As the wide implications of charge transfer are indisputable, using the tools at our disposal for improving the reliability of electroanalysis is crucial for advancing modern science and engineering.

Computational Estimation of the Acidities of Pyrimidines and Related Compounds

Pyrimidines are key components in the genetic code of living organisms and the pyrimidine scaffold is also found in many bioactive and medicinal compounds. The acidities of these compounds, as represented by their pKas, are of special interest since they determine the species that will prevail under different pH conditions. Here, a quantum chemical quantitative structure-activity relationship (QSAR) approach was employed to estimate these acidities. Density-functional theory calculations at the B3LYP/6-31+G(d,p) level and the SM8 aqueous solvent model were employed, and the energy difference ∆EH2O between the parent compound and its dissociation product was used as a variation parameter. Excellent estimates for both the cation → neutral (pKa1, R2 = 0.965) and neutral → anion (pKa2, R2 = 0.962) dissociations were obtained. A commercial package from Advanced Chemical Design also yielded excellent results for these acidities.

Protonation of Serine in Gas and Condensed and Microsolvated States in Aqueous Solution

Identification of molecules and elucidation of their chemical structure are ubiquitous problems in chemistry. Mass spectrometry (MS) can be used due to its sensitivity and versatility. For detection to occur, analytes must be ionized and transferred to the gas phase. Soft ionization processes such as electrospray ionization are popular; however, resulting microsolvated phases can alter the chemistry of analytes and therefore detection and identification. To understand these processes, we use computational methods to probe the ionization propensity of serine in the gas phase, aqueous microsolvated clusters, and aqueous solution. We show that the tautomeric form of serine is altered by the presence of water, as five water molecules can stabilize the zwitterionic tautomer. Inclusion of cosolutes such as ions can stabilize the zwitterion with as few as one or two water molecules present. We demonstrate that ionization propensity, as measured by gas phase bacisity, can increase by over 100 kJ/mol when placed in a small water-serine cluster, showing the sensitivity of the chemistry of microsolvated analytes. Finally, detailed analysis reveals that small droplets (less than seven water molecules) are extremely sensitive to addition of further water molecules. Beyond this limit, structural and electronic properties change little with droplet size.

Automated assessment of redox potentials for dyes in dye-sensitized photoelectrochemical cells

Sustainable solutions for hydrogen production, such as dye-sensitized photoelectrochemical cells (DS-PEC), rely on the fundamental properties of its components whose modularity allows for their separate investigation. In this work, we design and execute a high-throughput scheme to tune the ground state oxidation potential (GSOP) of perylene-type dyes by functionalizing them with different ligands. This allows us to identify promising candidates which can then be used to improve the cell's efficiency. First, we investigate the accuracy of different theoretical approaches by benchmarking them against experimentally determined GSOPs. We test different methods to calculate the vertical oxidation potential, including GW with different levels of self-consistency, Kohn-Sham (KS) orbital energies and total energy differences. We find that there is little difference in the performance of these methods. However, we show that it is crucial to take into account solvent effects as well as the structural relaxation of the dye after oxidation. Other thermodynamic contributions are negligible. Based on this benchmark, we decide on an optimal strategy, balancing computational cost and accuracy, to screen more than 1000 dyes and identify promising candidates which could be used to construct more robust DS-PECs.

Dipole Moment Calculations Using Multiconfiguration Pair-Density Functional Theory and Hybrid Multiconfiguration Pair-Density Functional Theory

The dipole moment is the molecular property that most directly indicates molecular polarity. The accuracy of computed dipole moments depends strongly on the quality of the calculated electron density, and the breakdown of single-reference methods for strongly correlated systems can lead to poor predictions of the dipole moments in those cases. Here, we derive the analytical expression for obtaining the electric dipole moment by multiconfiguration pair-density functional theory (MC-PDFT), and we assess the accuracy of MC-PDFT for predicting dipole moments at equilibrium and nonequilibrium geometries. We show that MC-PDFT dipole moment curves have reasonable behavior even for stretched geometries, and they significantly improve upon the CASSCF results by capturing more electron correlation. The analysis of a dataset consisting of 18 first-row transition-metal diatomics and 6 main-group polyatomic molecules with a multireference character suggests that MC-PDFT and its hybrid extension (HMC-PDFT) perform comparably to CASPT2 and MRCISD+Q methods and have a mean unsigned deviation of 0.2-0.3 D with respect to the best available dipole moment reference values. We explored the dependence of the predicted dipole moments upon the choice of the on-top density functional and active space, and we recommend the tPBE and hybrid tPBE0 on-top choices for the functionals combined with the moderate correlated-participating-orbitals scheme for selecting the active space. With these choices, the mean unsigned deviations (in debyes) of the calculated equilibrium dipole moments from the best estimates are 0.77 for CASSCF, 0.29 for MC-PDFT, 0.24 for HMC-PDFT, 0.28 for CASPT2, and 0.25 for MRCISD+Q. These results are encouraging because the computational cost of MC-PDFT or HMC-PDFT is largely reduced compared to the CASPT2 and MRCISD+Q methods.

SSIPTools: Software and Methodology for Surface Site Interaction Point (SSIP) Approach and Applications

We present the SSIPTools suite of programs. SSIPTools is a collection of software modules enabling the use of the Surface Site Interaction Point (SSIP) molecular descriptors, used for the modeling of noncovalent interactions in neutral organic molecules. It contains an implementation of the workflow for the generation of the SSIP descriptors, as well as the Functional Group Interaction Profiles (FGIPs) and Solvent Similarity Indexes (SSIs) applications, based on the SSIMPLE (Surface Site Interaction model for the Properties of Liquids at Equilibria) approach.

An empirical model for solvation based on surface site interaction points

Surface site interaction points (SSIP) provide a quantitative description of the non-covalent interactions a molecule makes with the environment based on specific intermolecular contacts, such as H-bonds. Summation of the free energy of interaction of each SSIP across the surface of a molecule allows calculation of solvation energies and partition coefficients. A rule-based approach to the assignment of SSIPs based on chemical structure has been developed, and a combination of experimental data on the formation of 1 : 1 H-bonded complexes in non-polar solvents and partition of solutes between different solvents was used to parameterise the method. The resulting model is simple to implement using just a spreadsheet and accurately describes the transfer of a wide range of different solutes from water to a wide range of different organic solvents (overall rmsd is 1.4 kJ mol-1 for 1713 data points). The hydrophobic effect as well as the properties of perfluorocarbon solvents are described well by the model, and new descriptors have been determined for range of organic solvents that were not accessible by direct investigation of H-bond formation in non-polar solvents.

Solvation Free Energies for Aqueous and Nonaqueous Solutions Computed Using PM7 Atomic Charges

We describe a simple and accurate method, ESE-PM7, for calculating solvation free energies ΔG_solv^° in aqueous and nonaqueous solutions. The method is based on a noniterative COSMO algorithm. Molecular geometries and atomic charges calculated using the semiempirical method PM7 are used to calculate ΔG_solv^°. The method has been tested on 92 different solvents and 988 solutes. The mean absolute errors (MAEs) in ΔG_solv^° in aqueous solutions estimated by the ESE-PM7 approach are found to be 1.62 kcal/mol for 389 neutral solutes and 3.06 kcal/mol for 139 ions. The MAEs for neutral molecules in organic solvents are 0.97, 0.74, and 0.51 kcal/mol in polar protic, polar aprotic, and nonpolar solvents, respectively. The developed method can be employed to quickly screen ΔG_solv^° values of extended molecular systems including pharmaceutical and biological molecules.