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.

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 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 molecular dynamics are the most common techniques for the prediction of thermodynamic properties, there exists many shortcomings associated with their use.

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.

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 of the SRS-MP2/cc-pVTZ reference values for the 3B69 and S22-3 trimer data sets.

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

The prediction of a molecule's solvation Gibbs free (Δ) 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 Δ 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.

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.

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.

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 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.

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.

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.

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.

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 . 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.

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.

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.

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.

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][BF].

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

Recent studies of alkali metal -(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.

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).

An 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 calculations.

Cluster approach to the prediction of thermodynamic and transport properties of ionic liquids.

The prediction of physicochemical properties of ionic liquids such as conductivity and melting point would substantially aid the targeted design of ionic liquids for specific applications ranging from solvents for extraction of valuable chemicals to biowaste to electrolytes in alternative energy devices. The previously published study connecting the interaction energies of single ion pairs (1 IP) of ionic liquids to their thermodynamic and transport properties has been extended to larger systems consisting of two ion pairs (2 IPs), in which many-body and same-ion interactions are included. Routinely used cations, of the imidazolium and pyrrolidinium families, were selected in the study coupled with chloride, tetrafluoroborate, and dicyanamide.

Raman spectroscopy as a tool for tracking cyclopropane fatty acids in genetically engineered Saccharomyces cerevisiae.

Cyclopropane fatty acids (CFAs) are a group of lipids with unique physical and chemical properties between those of saturated and monounsaturated fatty acids. The distinctive physicochemical characteristics of CFAs (e.g.

Is it possible to control kinetic rates of radical polymerisation in ionic liquids?

Experimental studies have noted the often surprising and unpredictable effect of ionic liquids as solvents on reaction kinetics for radical polymerisation. We theoretically investigate the energetic and structural effects of ionic liquids, both protic and aprotic, on radical stability, presenting stabilisation of the radical by the ionic liquid by up to -78.0 kJ mol-1.