Toward an extreme-scale electronic structure system.

Electronic structure calculations have the potential to predict key matter transformations for applications of strategic technological importance, from drug discovery to material science and catalysis. However, a predictive physicochemical characterization of these processes often requires accurate quantum chemical modeling of complex molecular systems with hundreds to thousands of atoms. Due to the computationally demanding nature of electronic structure calculations and the complexity of modern high-performance computing hardware, quantum chemistry software has historically failed to operate at such large molecular scales with accuracy and speed that are useful in practice. In this paper, novel algorithms and software are presented that enable extreme-scale quantum chemistry capabilities with particular emphasis on exascale calculations. This includes the development and application of the multi-Graphics Processing Unit (GPU) library LibCChem 2.0 as part of the General Atomic and Molecular Electronic Structure System package and of the standalone Extreme-scale Electronic Structure System (EXESS), designed from the ground up for scaling on thousands of GPUs to perform high-performance accurate quantum chemistry calculations at unprecedented speed and molecular scales. Among various results, we report that the EXESS implementation enables Hartree-Fock/cc-pVDZ plus RI-MP2/cc-pVDZ/cc-pVDZ-RIFIT calculations on an ionic liquid system with 623 016 electrons and 146 592 atoms in less than 45 min using 27 600 GPUs on the Summit supercomputer with a 94.6% parallel efficiency.

Negating coordinative cysteine and methionine residues during metathesis of unprotected peptides.

Ru-Alkylidene catalysed olefin metathesis generates metabolically stable cystine bridge peptidomimetics with defined geometry. Deleterious coordinative bonding to the catalyst by sulfur-containing functionality found in cysteine and methionine residues can be negated by in situ and reversible oxidation of thiol and thioether functionality, as disulfides and S-oxides respectively, to facilitate high yielding ring-closing and cross metathesis of bioorthogonally protected peptides.

Appropriate clusterset selection for the prediction of thermodynamic properties of liquid water with QCE theory.

Evident in many physical and chemical phenomena, thermodynamics is the study of how energy is stored, transformed and transferred in a molecule or material. However, prediction of these properties with simulation techniques is a non-trivial task as several factors such as composition and intermolecular interactions come into play. While molecular dynamics and ab initio molecular dynamics are the most common techniques for the prediction of thermodynamic properties, there exists many shortcomings associated with their use. Therefore, in this work we instead apply QCE theory to predict the thermodynamic properties of liquid water. This theory assumes that a condensed phase system can be represented as a 'mixture' of varying sized clusters rather than as a continuum. As QCE theory relies on first-principle simulations and statistical thermodynamics to determine the thermodynamic behavior of a system, appropriate selection of clusters is a crucial step towards achieving accurate predictions. In this study, we use molecular dynamics and ab initio calculations to obtain initial configurations of 400 water clusters, Wn where n = 3 to 10 and contrast their stability using two different criteria. The role of entropy towards cluster stabilization is investigated by comparing the binding (ΔEBIND/mol) and Gibbs free binding energy per molecule (ΔGBIND/mol) of various Wn at 298.15 K. Initial clustersets are constructed by exploring two-, three-, four and five-combinations of clustersets using the minimum ΔGBIND/mol structures of Wn. We also expand the ΔGBIND/mol criteria for Wn of sizes 3 to 7 to include values larger than 0.0 kJ mol-1 and smaller than 3.0 kJ mol-1 as a means of improving thermodynamic predictions. 37 of the 459 resulting clustersets predicted the correct boiling point of water and its thermodynamic properties with an accuracy of 95%. A scaled population-weighted infrared spectrum was compared to experimental results to validate the composition of the top 5 clustersets. The predicted spectra showed an exact match within the low frequency range (<1000 cm-1) with some discrepancy at the high frequency range (>3400 cm-1). This work highlights that ΔGBIND/mol is so far the best criteria to apply when determining an appropriate clusterset for QCE theory.

Structural elucidation of polydopamine facilitated by ionic liquid solvation.

Minimal understanding of the formation mechanism and structure of polydopamine (pDA) and its natural analogue, eumelanin, impedes the practical application of these versatile polymers and limits our knowledge of the origin of melanoma. The lack of conclusive structural evidence stems from the insolubility of these materials, which has spawned significantly diverse suggestions of pDA's structure in the literature. We discovered that pDA is soluble in certain ionic liquids. Using these ionic liquids (ILs) as solvents, we present an experimental methodology to solvate pDA, enabling us to identify pDA's chemical structure. The resolved pDA structure consists of self-assembled supramolecular aggregates that contribute to the increasing complexity of the polymer. The underlying molecular energetics of pDA solvation and a macroscopic picture of the disruption of the aggregates using IL solvents have been investigated, along with studies of the aggregation mechanism in water.

Accurate Prediction of Three-Body Intermolecular Interactions via Electron Deformation Density-Based Machine Learning.

This work extends the electron deformation density-based descriptor, originally developed in the electron deformation density-based interaction energy machine learning (EDDIE-ML) algorithm to predict dimer interaction energies, to the prediction of three-body interactions in trimers. Using a sequential learning process to select the training data, the resulting Gaussian process regression (GPR) model predicts the three-body interaction energy within 0.2 kcal mol-1 of the SRS-MP2/cc-pVTZ reference values for the 3B69 and S22-3 trimer data sets. A hybrid kernel function is introduced, which combines contributions from the average and individual atomic environments, allowing the total trimer interaction energy to be predicted in addition to the three-body contribution using the same descriptor. To extend the range and diversity of trimer interaction energies available in the literature, a new data set based on a protein-ligand crystal structure is introduced, consisting of 509 structures of a central ligand with two protein fragments. Benchmark calculations are provided for the new data set, which contains significantly larger molecular interactions than current databases in the literature in addition to charged fragments. Compared to density funtional theory (DFT)- and wavefunction-based methods for calculating the three-body interaction energy, our model makes predictions in a significantly shorter time frame by reducing the number of required SCF calculations from 7 to 4 performed at the PBE0 level of theory, showcasing the utility and efficiency of our Δ-ML method particularly when applied to larger systems.

Explainable Solvation Free Energy Prediction Combining Graph Neural Networks with Chemical Intuition.

The prediction of a molecule's solvation Gibbs free (ΔGsolv) energy in a given solvent is an important task which has traditionally been carried out via quantum chemical continuum methods or force field-based molecular simulations. Machine learning (ML) and graph neural networks in particular have emerged as powerful techniques for elucidating structure-property relationships. This work presents a graph neural network (GNN) for the prediction of ΔGsolv which, in addition to encoding typical atom and bond-level features, incorporates chemically intuitive, solvation-relevant parameters into the featurization process: semiempirical partial atomic charges and solvent dielectric constant. Solute-solvent interactions are included via an interaction map layer which can be visualized to examine solubility-enhancing or -decreasing interactions learnt by the model. On a test set of small organic molecules, our GNN predicts ΔGsolv in water and cyclohexane with an accuracy comparable to polarizable and ab initio generated force field methods [mean absolute error (MAE) = 0.4 and 0.2 kcal mol-1, respectively], without the need for any molecular simulation. For the FreeSolv data set of hydration free energies, the test MAE is 0.7 kcal mol-1. Interpretability and applicability of the model is highlighted through several examples including rationalizing the increased solubility of modified diaminoanthraquinones in organic solvents. The clear explanations afforded by our GNN allow for easy understanding of the model's predictions, giving the experimental chemist confidence in employing ML models toward more optimized synthetic routes.

Behavior of counterpoise correction in many-body molecular clusters of organic compounds: Hartree-Fock interaction energy perspective.

The counterpoise (CP) correction by Boys and Bernardi has been well accepted as a reliable strategy to account for basis set superposition error (BSSE) in intermolecular complexes. The behavior of the CP correction was thoroughly studied in individual molecules of molecular complexes. This work studies the performance of the CP correction in many-body clusters including three-body clusters of organic compounds in the 3B-69 dataset. Additionally, we used crystal structures of polymorphs of benzene, aspirin, and oxalyl dihydrazide (ODH) to construct a many-body cluster dataset, abbreviated as the MBC-36 dataset, consisting of two, four and eight molecules, and 16 molecules in the case of benzene. A series of Dunning's basis sets-cc-pXZ and aug-cc-pXZ (X = D and T)-were used to predict CP-corrected Hartree-Fock (HF) interaction energies of the 3B-69 and MBC-36 datasets. The CP-corrected interaction energies were found to be basis-set independent, whereas the non-CP corrected interaction energies were found not to a follow a smooth exponential fitting as previously found for electronic energies of individual molecules. This observation was attributed to the presence of non-additive induction forces in some clusters. Two 2 × 2 × 2 supercells of benzene polymorphs were constructed to explore the local nature of BSSE effects. A cut-off radius of 10 Å was demonstrated to be sufficient to fully recover these effects. Although the behavior of CP correction was found to be non-conventional in many-body clusters of organic compounds, the use of a small basis set such as cc-pVDZ showed excellent performance in the prediction of HF interaction energies.

Inclusion of More Physics Leads to Less Data: Learning the Interaction Energy as a Function of Electron Deformation Density with Limited Training Data.

Machine learning (ML) approaches to predicting quantum mechanical (QM) properties have made great strides toward achieving the computational chemist's holy grail of structure-based property prediction. In contrast to direct ML methods, which encode a molecule with only structural information, in this work, we show that QM descriptors improve ML predictions of dimer interaction energy, both in terms of accuracy and data efficiency, by incorporating electronic information into the descriptor. We present the electron deformation density interaction energy machine learning (EDDIE-ML) model, which predicts the interaction energy as a function of Hartree-Fock electron deformation density. We compare its performance with leading direct ML schemes and modern DFT methods for the prediction of interaction energies for dimers of varying charge type, size, and intermolecular separation. Under a low-data regime, EDDIE-ML outperforms other direct ML schemes and is the only model readily transferrable to larger, more complex systems including base pair trimers and porous cages. The underlying physical connection between the density and interaction energy enables EDDIE-ML to reach an accuracy comparable to modern DFT functionals in fewer training data points compared to other ML methods.

A DLPNO-CCSD(T) benchmarking study of intermolecular interactions of ionic liquids.

The accuracy of correlation energy recovered by coupled cluster single-, double-, and perturbative triple-excitations, CCSD(T), has led to the method being considered the gold standard of computational chemistry. The application of CCSD(T) has been limited to medium-sized molecular systems due to its steep scaling with molecular size. The recent development of alternative domain-based local pair natural orbital coupled-cluster method, DLPNO-CCSD(T), has significantly broadened the range of chemical systems to which CCSD(T) level calculations can be applied. Condensed systems such as ionic liquids (ILs) have a large contribution from London dispersion forces of up to 150 kJ mol-1 in large-scale clusters. Ionic liquids show appreciable charge transfer effects that result in the increased valence orbital delocalization over the entire ionic network, raising the question whether the application of methods based on localized orbitals is reliable for these semi-Coulombic materials. Here the performance of DLPNO-CCSD(T) is validated for the prediction of correlation interaction energies of two data sets incorporating single-ion pairs of protic and aprotic ILs. DLPNO-CCSD(T) produced results within chemical accuracy with tight parameter settings and a non-iterative treatment of triple excitations. To achieve spectroscopic accuracy of 1 kJ mol-1 , especially for hydrogen-bonded ILs and those containing halides, the DLPNO settings had to be increased by two orders of magnitude and include the iterative treatment of triple excitations, resulting in a 2.5-fold increase in computational cost. Two new sets of parameters are put forward to produce the performance of DLPNO-CCSD(T) within chemical and spectroscopic accuracy.

Establishing the accuracy of density functional approaches for the description of noncovalent interactions in ionic liquids.

We test a number of dispersion corrected versatile Generalized Gradient Approximation (GGA) and meta-GGA functionals for their ability to predict the interactions of ionic liquids, and show that most can achieve energies within 1 kcal mol-1 of benchmarks. This compares favorably with an accurate dispersion corrected hybrid, ωB97X-V. Our tests also reveal that PBE (Perdew-Burke-Ernzerhof GGA) calculations using the plane-wave projector augmented wave method and Gaussian Type Orbitals (GTOs) differ by less than 0.6 kJ mol-1 for ionic liquids, despite ions being difficult to evaluate in periodic cells - thus revealing that GTO benchmarks may be used also for plane-wave codes. Finally, the relatively high success of explicit van der Waals density functionals, compared to elemental and ionic dispersion models, suggests that improvements are required for low-cost dispersion correction models of ions.

Experimental Evidence of Long-Lived Electric Fields of Ionic Liquid Bilayers.

Herein we demonstrate that ionic liquids can form long-lived double layers, generating electric fields detectable by straightforward open circuit potential (OCP) measurements. In imidazolium-based ionic liquids an external negative voltage pulse leads to an exceedingly stable near-surface dipolar layer, whose field manifests as long-lived (∼1-100 h) discrete plateaus in OCP versus time traces. These plateaus occur within an ionic liquid-specific and sharp potential window, defining a simple experimental method to probe the onset of interfacial ordering phenomena, such as overscreening and crowding. Molecular dynamics modeling reveals that the OCP arises from the alignment of the individual ion dipoles to the external electric field pulse, with the magnitude of the resulting OCP correlating with the product of the projected dipole moment of the cation and the ratio between the cation diffusion coefficient and its volume. Our findings also reveal that a stable overscreened structure is more likely to form if the interface is first forced through crowding, possibly accounting for the scattered literature data on relaxation kinetics of near-surface structures in ionic liquids.

Electrochemical characterization and thermodynamic analysis of TEMPO derivatives in ionic liquids.

In this study we investigate the reversibility of the reduction process of three TEMPO derivatives - TEMPOL, 4-cyano-TEMPO, and 4-oxo-TEMPO. The [C2mim][BF4] and [C4mpyr][OTf] ionic liquids (ILs) were used to perform cyclic voltammetry (CV) to analyse the redox potentials of the TEMPO derivatives. The former was previously shown to quench the aminoxy anion of TEMPO through a proton transfer reaction with the cation, whereas the latter supported the irreversibility of the TEMPO reduction process. In CV results on TEMPO derivatives, it was shown that [C4mpyr][OTf] could allow for a high degree of reversibility in the reduction of 4-cyano-TEMPO and a moderate degree of reversibility in the reduction of TEMPOL. In comparison, reduction of 4-cyano-TEMPO was predominantly irreversible in [C2mim][BF4], whilst TEMPOL showed complete irreversibility. 4-Oxo-TEMPO did not show any notable reduction reversibility in either IL tested. Reduction potentials showed little variation between the derivatives and 0.2 V variation between the ILs, with the most negative reduction potential being observed at -1.43 V vs. Fc/Fc+ for TEMPOL in [C4mpyr][OTf]. To explain the varying degrees of reversibility of the reduction process, four types of side reactions involving proton transfer to the aminoxy anion were studied using highly correlated quantum chemical methods. Proton transfer from the IL cation was shown to have the ability to quench all three aminoxy anions depending on the IL used. On average, TEMPOL was shown to be the most susceptible to proton transfer from the IL cation, having an average Gibbs free energy (GFE) of 10.5 kJ mol-1 more negative than that of 4-cyano-TEMPO, which was shown to have the highest GFE of proton transfer. Side reactions between water and aminoxy anions were also seen to have the potential to contribute to degradation of the aminoxy anions tested, with 4-oxo-TEMPO being shown to be the most reactive to degradation with water with a GFE of -12.6 kJ mol-1. 4-Oxo-TEMPO was found to be highly susceptible to self-quenching by its aminoxy anion and radical form with highly negative proton transfer GFEs of -47.9 kJ mol-1 and -57.7 kJ mol-1, respectively. Overall, 4-cyano-TEMPO is recommended as being the most stable of the aminoxy anions tested with TEMPOL, thus providing a viable alternative to improve solubility should the IL be tuned to maximize its stability.

Prediction of lattice energy of benzene crystals: A robust theoretical approach.

We present an inexpensive and robust theoretical approach based on the fragment molecular orbital methodology and the spin-ratio scaled second-order Møller-Plesset perturbation theory to predict the lattice energy of benzene crystals within 2 kJ⋅mol-1 . Inspired by the Harrison method to estimate the Madelung constant, the proposed approach calculates the lattice energy as a sum of two- and three-body interaction energies between a reference molecule and the surrounding molecules arranged in a sphere. The lattice energy converges rapidly at a radius of 13 Å. Adding the corrections to account for a higher correlated level of theory and basis set superposition for the Hartree Fock (HF) level produced a lattice energy of -57.5 kJ⋅mol-1 for the benzene crystal structure at 138 K. This estimate is within 1.6 kJ⋅mol-1 off the best theoretical prediction of -55.9 kJ⋅mol-1 . We applied this approach to calculate lattice energies of the crystal structures of phase I and phase II-polymorphs of benzene-observed at a higher temperature of 295 K. The stability of these polymorphs was correctly predicted, with phase II being energetically preferred by 3.7 kJ⋅mol-1 over phase I. The proposed approach gives a tremendous potential to predict stability of other molecular crystal polymorphs.

Predicting Entropic Effects of Water Mixing with Ionic Liquids Containing Anions of Strong Hydrogen Bonding Ability: Role of the Cation.

Ionic liquids (ILs) such as choline dihydrogen phosphate exhibit an extraordinary solubilizing ability for proteins such as cytochrome C when mixed with 20 wt % water. Most widely used imidazolium-based ionic liquids coupled with dihydrogen phosphate do not exhibit the same solubilizing properties, suggesting that a multifunctional cation such as choline might play a key role in enhancing these properties of ionic liquid mixtures with water. In this theoretical work, we compare intermolecular interactions between the water molecule and ionic liquid ions in two ion-paired clusters of choline- and 1-butyl-3-methyl-imidazolium-based ionic liquids coupled with acetate, dihydrogen phosphate, and mesylate. Gibbs free energy (GFE) of solvation of water in these ionic liquids was calculated. Incorporation of a water molecule into ionic liquid clusters was accompanied by negative GFEs of solvation in both types of cations. These results were in good agreement with previously reported experimental GFEs of solvation of water in ILs. Compared to imidazolium-based clusters, strong interionic interactions of choline ionic liquids resulted in more negative GFEs due to their smaller deformation upon the addition of a water molecule, with dihydrogen phosphate and mesylate predicting the lowest GFEs of -30.1 and -43.5 kJ/mol-1, respectively. Lower GFEs of solvation of water in choline-based clusters were also accompanied with smaller entropic penalties, suggesting that water easily incorporates itself into the existing ionic network. Analysis of the intramolecular bonds within the water molecule showed that the choline hydroxyl group donates electron density to the neighboring water molecule, leading to additional polarization. The predicted infrared spectra of clusters of ionic liquids with water showed a pronounced red shift due to strongly polarized O-H bonds, in excellent agreement with the experimentally measured infrared spectra of ionic liquid mixtures with water. Increased polarization of water in choline-based ionic liquids undoubtedly creates more effective solvents for stabilizing biological molecules such as proteins.

A Systematic Study of DFT Performance for Geometry Optimizations of Ionic Liquid Clusters.

Clusters of two ion pairs of imidazolium-based ionic liquids were optimized with 43 different levels of theory, including DFT functionals and MP2-based methods combined with varying Dunning's basis sets, and added dispersion corrections. Better preforming DFT functionals were then applied to clusters consisting of four ion pairs. Excellent performance of some DFT functionals for the two ion pair clusters did not always match that of the four ion-paired clusters despite interionic distances remaining constant between the optimized two and four ion-paired clusters of the same ionic liquid. Combinations of DFT functional and basis set such as ωB97X-D/cc-pVDZ, M06-2X/aug-cc-pVDZ, B3LYP-D3/cc-pVTZ, and TPSS-D3/cc-pVTZ gave excellent results for geometry optimization of two ion-paired clusters of imidazolium ionic liquids but gave larger deviations when applied to the four ion-paired clusters of varying ionic liquids. Empirical dispersion corrections were seen to be crucial in correctly capturing correlation effects in the studied ionic liquid clusters, becoming more important in larger clusters. Dunning's double-ζ basis set, cc-pVDZ, is associated with the smallest root mean squared deviations for geometries; however, it also produces the largest deviations in total electronic energies. ωB97X-D and M06-2X produced the best performance with the augmented version of this basis set. The triple-ζ basis set, cc-pVTZ, leads to the best performance of most of the DFT functionals (especially the dispersion-corrected ones) used, whereas its augmented version, aug-cc-pVTZ, was not seen to improve results. The combinations of functional and basis set that gave the best geometry and energetics in both two and four ion-paired clusters were PBE-D3/cc-pVTZ, ωB97X-D/aug-cc-pVDZ, and BLYP-D3/cc-pVTZ. All three combinations are recommended for geometry optimizations of larger clusters of ionic liquids. PBE-D3/cc-pVTZ performed the best with an average deviation of 2.3 kJ mol-1 and a standard deviation of 3.4 kJ mol-1 for total electronic energy when applied to four ion-paired clusters. Geometries optimized with FMO2-SRS-MP2/cc-pVTZ produced total energy within 2.0 kJ mol-1 off the benchmark in two ion-paired clusters, with the cc-pVDZ basis set performing unsurprisingly poorly with the same method. The error increased to 4.8 kJ mol-1 on average in four ion-paired clusters, with the smallest RMSD deviations in geometries when compared to the benchmark ones. This study is the first report that investigated the performance of DFT functionals for two and four ion-paired clusters of a wide range of ionic liquids consisting of commonly used cations such as pyrrolidinium, imidazolium, pyridinium, and ammonium. It also identified the importance of assessing the performance of quantum chemical methods for ionic liquids on a variety of cation-anion combinations.

The effect of descriptor choice in machine learning models for ionic liquid melting point prediction.

The characterization of an ionic liquid's properties based on structural information is a longstanding goal of computational chemistry, which has received much focus from ab initio and molecular dynamics calculations. This work examines kernel ridge regression models built from an experimental dataset of 2212 ionic liquid melting points consisting of diverse ion types. Structural descriptors, which have been shown to predict quantum mechanical properties of small neutral molecules within chemical accuracy, benefit from the addition of first-principles data related to the target property (molecular orbital energy, charge density profile, and interaction energy based on the geometry of a single ion pair) when predicting the melting point of ionic liquids. Out of the two chosen structural descriptors, ECFP4 circular fingerprints and the Coulomb matrix, the addition of molecular orbital energies and all quantum mechanical data to each descriptor, respectively, increases the accuracy of surrogate models for melting point prediction compared to using the structural descriptors alone. The best model, based on ECFP4 and molecular orbital energies, predicts ionic liquid melting points with an average mean absolute error of 29 K and, unlike group contribution methods, which have achieved similar results, is applicable to any type of ionic liquid.

Ordered Solvents and Ionic Liquids Can Be Harnessed for Electrostatic Catalysis.

Herein, we employ classical molecular dynamics simulations using the Drude oscillator-based polarizable force field, quantum chemical calculations, and ONIOM multiscale calculations to study (a) how an external field orders the solvent environment in a chemical reaction and then (b) whether in the absence of this same applied field the ordered solvent environment alone can electrostatically catalyze a chemical reaction when compared with the corresponding disordered solvent. Our results show that a 0.2 V/Å external electric field, which is below the threshold for bond breaking of solvent molecules, leads to significant ordering of bulk methanol solvent and the ionic liquid [EMIM][BF4]. Importantly, in the absence of this same field, the ordered solvent lowers the activation energy of the hydrogen-transfer reaction of o-alkylphenyl ketones in excess of 20 kcal/mol when the solvent is methanol and by over 30 kcal/mol for [EMIM][BF4]. Even a 0.1 V/Å external field has effects of ca. 10 and 20 kcal/mol, respectively. This work suggests a possible strategy for scaling electrostatic catalysis by applying a pulsed external field to the reaction medium to maintain solvent ordering while allowing the reaction to proceed largely in the absence of an external field.

Isomers of Alkali Metal (Methylbenzyl)allylamides: A Theoretical Perspective.

Recent studies of alkali metal N-(α-methylbenzyl)allylamides containing lithium, sodium, and potassium have shown unique rearrangements in NMR experiments. It was found that lithium isomers favored the formation of aza-allyl and aza-enolate complexes that could exist in a solution for a substantial amount of time. As the radius of the metal ion increases going from lithium to potassium, so does the preference for the formation of the imine structure. For sodium, the aza-allyl complex could still be isolated, whereas the imine structure was only found to be stable on the scale of several hours for potassium. In this work, ab initio calculations were used to shed light on this phenomenon. Decomposition of intermolecular interaction energies of the aza-allyl, aza-enolate, and imine complexes showed that for lithium, the formation of aza-allyl and aza-enolate complexes was driven by electrostatic interactions. For potassium, the dispersion component of the metal interaction with the ligand proved to be more important for the stability of the imine structure. The presence of the imine formation in potassium and partially in sodium was found to be due to the reduced electrostatic nature of these larger metals. The assignment of the experimental NMR spectra was further confirmed with the natural bond order (NBO) analysis as well as the partial charge calculations. Analysis of orbital energies, specifically those of the highest occupied molecular orbitals (HOMOs), as well as the deformation energies of each of the ligands, were also considered. Through these procedures, an understanding of the tendency for each metal to have a unique isomerization pathway was gained.

Influence of DFT Functionals and Solvation Models on the Prediction of Far-Infrared Spectra of Pt-Based Anticancer Drugs: Why Do Different Complexes Require Different Levels of Theory?

Computational modeling was applied to far-infrared (FIR) spectra of Pt-based anticancer drugs to study the hydrolysis of these important molecules. Here, we present a study that investigates the influence of different factors-basis sets on non-Pt atoms, relativistic effective core potentials (RECPs) on the Pt atom, density functional theory (DFT) functionals, and solvation models-on the prediction of FIR spectra of two Pt-based anticancer drugs, cisplatin and carboplatin. Geometry optimizations and frequency calculations were performed with a range of functionals (PBE, PBE0, M06-L, and M06-2X), Dunning's correlation-consisted basis sets (VDZ, VTZ, aVDZ, and aVTZ), RECPs (VDZ-pp, VTZ-pp, aVDZ-pp, and aVTZ-pp), and solvation models (IEFPCM, CPCM, and SMD). The best combination of the basis set/DFT functional/solvation model was identified for each anticancer drug by comparing with experimentally available FIR spectra. Different combinations were established for cisplatin and carboplatin, which was rationalized by means of the partial atomic charge scheme, ChelpG, that was utilized to study the charge transfer between the Pt ion and ligands in both cisplatin and carboplatin.

An ab initio Study of the Structure and Energetics of Hydrogen Bonding in Ionic Liquids.

Unlike typical hydrogen-bonded networks such as water, hydrogen bonded ionic liquids display some unusual characteristics due to the complex interplay of electrostatics, polarization, and dispersion forces in the bulk. Protic ionic liquids in particular contain close-to traditional linear hydrogen bonds that define their physicochemical properties. This work investigates whether hydrogen bonded ionic liquids (HBILs) can be differentiated from aprotic ionic liquids with no linear hydrogen bonds using state-of-the-art ab initio calculations. This is achieved through geometry optimizations of a series of single ion pairs of HBILs in the gas phase and an implicit solvent. Using benchmark CCSD(T)/CBS calculations, the electrostatic and dispersion components of the interaction energy of these systems are compared with those of aprotic ionic liquids. The inclusion of the implicit solvent significantly influenced geometries of single ion pairs, with the gas phase shortening the hydrogen bond to reduce electrostatic interactions. HBILs were found to have stronger interactions by at least 10EtMeNH0 kJ mol-1 over aprotic ILs, clearly highlighting the electrostatic nature of hydrogen bonding. Geometric and energetic parameters were found to complement each other in determining the extent of hydrogen bonding present in these ionic liquids.