A first-principles alternative to empirical solvent parameters.

The use of solvents is ubiquitous in chemistry. Empirical parameters, such as the Kamlet-Taft parameters and Gutmann donor/acceptor numbers, have long been used to predict and quantify the effects solvents have on chemical phenomena. Collectively however, such parameters are unsatisfactory, since each describes ultimately the same non-covalent solute-solvent and solute-solute interactions in completely disparate ways. Here we hypothesise that empirical solvent parameters are essentially proxy measures of the electrostatic terms that dominate solvent-solute interactions. On the basis of this hypothesis, we develop a new fundamental descriptor of these interactions, , and show that it is a self-consistent, probe-free, first principles alternative to established empirical solvent parameters.

Dynamic Ion Correlations and Ion-Pair Lifetimes in Aqueous Alkali Metal Chloride Electrolytes.

Electrolytes are central to many technological applications, as well as life itself. The behavior and properties of electrolytes are often described in terms of ion pairs, whereby ions associate as either contact ion pairs (in which ions are "touching") solvent-separated ion pairs (in which ions' solvent shells overlap) or solvent-solvent-separated ion pairs (in which ions' solvent shells are distinct). However, this paradigm is generally restricted to statistically averaged descriptions of solution structure and ignores temporal behavior. Here we elucidate the time-resolved dynamics of these ion-ion interactions in aqueous metal chloride electrolytes using the partial van Hove correlation function, based on polarizable molecular dynamics simulations. Our results show that the existence and persistence of ion pairs in aqueous metal chloride electrolytes should not be assumed a priori, but in fact are ion specific features of the solution with lifetimes on subpicosecond time scales.

Discovery of Graphene Growth Alloy Catalysts Using High-Throughput Machine Learning.

Despite today's commercial-scale graphene production using chemical vapor deposition (CVD), the growth of high-quality single-layer graphene with controlled morphology and crystallinity remains challenging. Considerable effort is still spent on designing improved CVD catalysts for producing high-quality graphene. Conventionally, however, catalyst design has been pursued using empirical intuition or trial-and-error approaches. Here, we combine high-throughput density functional theory and machine learning to identify new prospective transition metal alloy catalysts that exhibit performance comparable to that of established graphene catalysts, such as Ni(111) and Cu(111). The alloys identified through this process generally consist of combinations of early- and late-transition metals, and a majority are alloys of Ni or Cu. Nevertheless, in many cases, these conventional catalyst metals are combined with unconventional partners, such as Zr, Hf, and Nb. The approach presented here therefore highlights an important new approach for identifying novel catalyst materials for the CVD growth of low-dimensional nanomaterials.

High-Throughput Aqueous Electrolyte Structure Prediction Using IonSolvR and Equivariant Graph Neural Network Potentials.

Neural network potentials have recently emerged as an efficient and accurate tool for accelerating ab initio molecular dynamics (AIMD) in order to simulate complex condensed phases such as electrolyte solutions. Their principal limitation, however, is their requirement for sufficiently large and accurate training sets, which are often composed of Kohn-Sham density functional theory (DFT) calculations. Here we examine the feasibility of using existing density functional tight-binding (DFTB) molecular dynamics trajectory data available in the IonSolvR database in order to accelerate the training of E(3)-equivariant graph neural network potentials. We show that the solvation structure of Na+ and Cl- in aqueous NaCl solutions can be accurately reproduced with remarkably small amounts of data (i.e., 100 MD frames). We further show that these predictions can be systematically improved further via an embarrassingly parallel resampling approach.

Specific Ion Effects at the Vapor-Formamide Interface: A Reverse Hofmeister Series in Ion Concentration Depth Profiles.

Employing neutral impact collision ion scattering spectroscopy (NICISS), we have directly measured the concentration depth profiles (CDPs) of various monovalent ions at the vapor-formamide interface. NICISS provides CDPs of individual ions by measuring the energy loss of neutral helium atoms backscattered from the solution interface. CDPs at the vapor-formamide interface of Cl-, Br-, I-, Na+, K+, and Cs+ are measured and compared to elucidate the interfacial specific ion trends. We report a reverse Hofmeister series in the presence of inorganic ions (anion and cation) at the vapor-formamide interface relative to the water-vapor interface, and the CDPs are found to be independent of the counterion for most ions studied. Thus, ions at the surface of formamide follow a "Hofmeister paradigm" where the counterion does not impact the ion series. These specific ion trends are complemented with surface tension and X-ray absorption near-edge structure (XANES) measurements on formamide electrolyte solutions.

Hofmeister effects influence bulk nanostructure in a protic ionic liquid.

HYPOTHESIS: The origins and behaviour of specific ion effects have been studied in water for more than a century, and more recently in nonaqueous molecular solvents. However, the impacts of specific ion effects on more complex solvents such as nanostructured ionic liquids remains unclear. Here, we hypothesise that the influence of dissolved ions on the hydrogen bonding in the nanostructured ionic liquid propylammonium nitrate (PAN) constitutes a specific ion effect. EXPERIMENTS: We performed molecular dynamics simulations of bulk PAN and 1-50 mol% PAN-PAX (X = halide anions F-, Cl-, Br-, I-) and PAN-YNO3 (Y = alkali metal cations, Li+, Na+, K+ and Rb+) solutions to investigate how monovalent salts influence the bulk nanostructure in PAN. FINDINGS: The key structural characteristic in PAN is a well-defined hydrogen bond network formed within the polar and non-polar domains in its nanostructure. We show that dissolved alkali metal cations and halide anions have significant and unique influences on the strength of this network. Cations (Li+, Na+, K+ and Rb+) consistently promote hydrogen bonding in the PAN polar domain. Conversely, the influence of halide anions (F-, Cl-, Br-, I-) is ion specific; while F- disrupts PAN hydrogen bonding, I- promotes it. The manipulation of PAN hydrogen bonding therefore constitutes a specific ion effect - i.e. a physicochemical phenomena caused by the presence of dissolved ions, which are dependent on these ions' identity. We analyse these results using a recently proposed predictor of specific ion effects developed for molecular solvents, and show that it is also capable of rationalising specific ion effects in the more complex solvent environment of an ionic liquid.

Role of Graphitic Bowls in Temperature Dependent Fullerene Formation.

Fullerenes are used extensively in organic electronics as electron acceptors among other uses; however, there are still several key mysteries regarding their formation such as the importance of graphitic intermediates and the thermokinetics of initial cage formation. To this end, we have conducted density functional tight binding molecular dynamics (DFTB-MD) calculations on disintegrated Ih-C60 to investigate the formation mechanisms of fullerenes at high temperature conditions. From the results of these DFTB-MD calculations we were able to develop a thermokinetic model to describe the free energies and kinetics of fullerene formation at a range of temperatures. Direct observation of the mechanism revealed fullerenes readily forming in nanosecond times between 2000 and 3000 K but were hindered above this temperature window. Analysis revealed temperature dependent formation mechanisms where at low temperatures (<2750K) flat graphitic bowls play an important part as metastable intermediates while highly curved bowls follow a direct fast transformation. Meanwhile at higher temperatures (>2750 K), flat bowls become the transitory structure between chains and fullerene. Free energy analysis from our thermokinetic model shows this change in graphitic bowls to being transitory hinders fullerene formation at high temperatures compared to lower temperatures, essentially kinetically trapping C60 as chain networks. This investigation gives new key insights into the formation mechanisms of C60 fullerenes and highlights important intermediates while also illuminating the temperature window for fullerene formation, facilitating better optimization of experimental methods.

Dithiocarbazate Ligand-Based Cu(II), Ni(II), and Zn(II) Complexes: Synthesis, Structural Investigations, Cytotoxicity, DNA Binding, and Molecular Docking Studies.

S-4-methylbenzyl-ß-N-(2-methoxybenzylmethylene)dithiocarbazate ligand, 1, prepared from S-(4-methylbenzyl)dithiocarbazate, was used to produce a novel series of transition metal complexes of the type, [M (L)2] [M = Cu(II) (2), Ni(II) (3), and Zn(II) (4), L = 1]. The ligand and its complexes were investigated by elemental analysis, FTIR, 1H and 13C-NMR, MS spectrometry, and molar conductivity. In addition, single X-ray crystallography was also performed for ligand, 1, and complex 3. The Hirshfeld surface analyses were also performed to know about various bonding interactions in the ligand, 1, and complex 3. The investigated compounds were also tested to evaluate their cytotoxic behaviour. However, complex 2 showed promising results against MCF-7 and MDA-MB-213 cancer cell lines. Furthermore, the interaction of CT-DNA with ligand, 1, and complex 2 was also studied using the electronic absorption method, revealing that the compounds have potential DNA-binding ability via hydrogen bonding and hydrophobic and van der Waals interactions. A molecular docking study of complex 2 was also carried out, which revealed that free binding free energy value was -7.39 kcal mol-1.

A quantum chemical molecular dynamics repository of solvated ions.

The importance of ion-solvent interactions in predicting specific ion effects in contexts ranging from viral activity through to electrolyte viscosity cannot be underestimated. Moreover, investigations of specific ion effects in nonaqueous systems, highly relevant to battery technologies, biochemical systems and colloid science, are severely limited by data deficiency. Here, we report IonSolvR - a collection of more than 3,000 distinct nanosecond-scale ab initio molecular dynamics simulations of ions in aqueous and non-aqueous solvent environments at varying effective concentrations. Density functional tight binding (DFTB) is used to detail the solvation structure of up to 55 solutes in 28 different protic and aprotic solvents. DFTB is a fast quantum chemical method, and as such enables us to bridge the gap between efficient computational scaling and maintaining accuracy, while using an internally-consistent simulation technique. We validate the database against experimental data and provide guidance for accessing individual IonSolvR records.

Ion specificity in the measured concentration depth profile of ions at the Vapor-Glycerol interface.

HYPOTHESIS: Specific ion effects are manifest universally across many systems and solvents. Whilst broad understanding of these effects is emerging particularly for bulk effects, the perturbation introduced by the interfaces are generally not understood. We hypothesise that through a careful investigation of the distribution of ions at the glycerol-vapor interface we can better understand specific ion effects in this system and at interfaces. EXPERIMENTS: Neutral impact collision ion scattering spectroscopy (NICISS) is used to obtain and compare individual ion concentration depth profiles (CDP) for a range of monovalent inorganic anions and cations at 12 glycerol electrolyte solutions surfaces. FINDINGS: The distribution of ions at the vapor - glycerol interface is non-monotonic. Broadly, anions are concentrated at the outermost region of the interface and cations are depleted from the interface. The distribution of Cl- and I- is mostly independent of the counterion. However, for Br- ions the distribution depends on the counterion where Cs+, K+, and Na+ ions lead to a desorption of Br- ions from the interface. This is favoured by the large solvation energy of Br- ions and consistent with the law of matching effective ion sizes.

Understanding specific ion effects and the Hofmeister series.

Specific ion effects (SIE), encompassing the Hofmeister Series, have been known for more than 130 years since Hofmeister and Lewith's foundational work. SIEs are ubiquitous and are observed across the medical, biological, chemical and industrial sciences. Nevertheless, no general predictive theory has yet been able to explain ion specificity across these fields; it remains impossible to predict when, how, and to what magnitude, a SIE will be observed. In part, this is due to the complexity of real systems in which ions, counterions, solvents and cosolutes all play varying roles, which give rise to anomalies and reversals in anticipated SIEs. Herein we review the historical explanations for SIE in water and the key ion properties that have been attributed to them. Systems where the Hofmeister series is perturbed or reversed are explored, as is the behaviour of ions at the liquid-vapour interface. We discuss SIEs in mixed electrolytes, nonaqueous solvents, and in highly concentrated electrolyte solutions - exciting frontiers in this field with particular relevance to biological and electrochemical applications. We conclude the perspective by summarising the challenges and opportunities facing this SIE research that highlight potential pathways towards a general predictive theory of SIE.

Why Carbon Nanotubes Grow.

Despite three decades of intense research efforts, the most fundamental question "why do carbon nanotubes grow?" remains unanswered. In fact, carbon nanotubes (CNTs) should not grow since the encapsulation of a catalyst with graphitic carbon is energetically more favorable than CNT growth in every aspect. Here, we answer this question using a theoretical model based on extensive first-principles and molecular dynamics calculations. We reveal a historically overlooked yet fundamental aspect of the CNT-catalyst interface, viz., that the interfacial energy of the CNT-catalyst edge is contact angle-dependent. The contact angle increases via graphitic cap lift-off, drastically decreasing the interfacial formation energy by up to 6-9 eV/nm, overcoming van der Waals cap-catalyst adhesion, and driving CNT growth. Mapping this remarkable and simple interplay allows us to understand, for the first time, why CNTs grow.

Deep eutectic solvents-The vital link between ionic liquids and ionic solutions.

When selecting a solvent for a given solute, the strongly held idiom "like dissolves like", meaning that polar solvents are used for polar solutes, is often used. This idea has resulted from the concept that most molecular solvents are homogeneous. In a deep eutectic solvent (DES), however, both components can be ionic or non-ionic, polar or non-polar. By tuning the components, DESs can solubilize a wide variety of solutes, often mixing hydrophobic and hydrophilic components, and the mixture can be designed to control phase behavior. The liquids often contain significant short-length order, and preferential solvation of one component often occurs. The addition of small polar molecules such as water or alcohols results in non-homogeneous liquids, which have significantly decreased viscosity and increased ionic conductivity. Accordingly, the areas covered in this special issue focus on structure and dynamics, solvation, the mobility of charged species, and the ability to obtain controllable phase behavior by adding polar diluents or using hydrophobic DESs.

Low-Cost Pt Alloys for Heterogeneous Catalysis Predicted by Density Functional Theory and Active Learning.

Pt is a key high-performing catalyst for important chemical conversions, such as biomass conversion and water splitting. Limited Pt reserves, however, demand that we identify more sustainable alternative catalyst materials for these processes. Here, we combine state-of-the-art graph neural networks and crystal graph machine learning representations with active learning to discover new, low-cost Pt alloy catalysts for biomass reforming and hydrogen evolution reactions. We identify 12 Pt-based alloys which have comparable catalytic activity to that of the exemplar Pt(111) surface. Notably, Cu3Pt and FeCuPt2 exhibit near identical catalytic performance as that of Pt(111). These results demonstrate the potential of machine learning for predicting new catalytic materials without recourse to expensive DFT geometry optimizations, the current bottleneck impeding high-throughput materials discovery. We also examine the performance of d-band theory in elucidating trends in binary and ternary Pt alloys.

Group and Period-Based Representations for Improved Machine Learning Prediction of Heterogeneous Alloy Catalysts.

Machine learning has recently emerged as an efficient and powerful alternative to density functional theory for studying heterogeneous catalysis. Machine learning methods rely on a geometrical representation of the chemical environment around the catalytic adsorption site based on physical or chemical descriptors. Here, we show that replacing the atomic number in geometrical representations with elemental groups and periods (GP) yields significant improvements in predicted adsorption energies on bimetallic alloy surfaces. Notably, the GP-based Labeled Site Crystal Graph representation reported here achieves mean absolute error (MAE) ∼0.05 eV (near chemical accuracy) in predicting hydrogen adsorption and MAE ∼0.10 eV for other strong binding adsorbates such as carbon, nitrogen, oxygen, and sulfur. We also show GP-based representations to be robust in predicting adsorption on surface facets, elements, and alloys that are not included in the initial training set. This reliability makes GP-based representations an ideal basis for high-throughput approaches and materials discovery based on active learning techniques, which often involve limited training sets.

Reaction pathways in the solid state and the Hubbard U correction.

We investigate how the Hubbard U correction influences vacancy defect migration barriers in transition metal oxide semiconductors. We show that, depending on the occupation of the transition metal d orbitals, the Hubbard U correction can cause severe instabilities in the migration barrier energies predicted using generalized gradient approximation density functional theory (GGA DFT). For the d0 oxide SrTiO3, applying a Hubbard correction to the Ti4+ 3d orbitals below 4-5 eV yields a migration barrier of ∼0.4 eV. However, above this threshold, the barrier increases suddenly to ∼2 eV. This sudden increase in the transition state barrier arises from the Hubbard U correction changing the Ti4+ t2g/eg orbital occupation, and hence electron density localization, along the migration pathway. Similar results are observed in the d10 oxide ZnO; however, significantly larger Hubbard U corrections must be applied to the Zn2+ 3d orbitals for the same instability to be observed. These results highlight important limitations to the application of the Hubbard U correction when modeling reactive pathways in solid state materials using GGA DFT.

The electrostatic origins of specific ion effects: quantifying the Hofmeister series for anions.

Life as we know it is dependent upon water, or more specifically salty water. Without dissolved ions, the interactions between biological molecules are insufficiently complex to support life. This complexity is intimately tied to the variation in properties induced by the presence of different ions. These specific ion effects, widely known as Hofmeister effects, have been known for more than 100 years. They are ubiquitous throughout the chemical, biological and physical sciences. The origin of these effects and their relative strengths is still hotly debated. Here we reconsider the origins of specific ion effects through the lens of Coulomb interactions and establish a foundation for anion effects in aqueous and non-aqueous environments. We show that, for anions, the Hofmeister series can be explained and quantified by consideration of site-specific electrostatic interactions. This can simply be approximated by the radial charge density of the anion, which we have calculated for commonly reported ions. This broadly quantifies previously unpredictable specific ion effects, including those known to influence solution properties, virus activities and reaction rates. Furthermore, in non-aqueous solvents, the relative magnitude of the anion series is dependent on the Lewis acidity of the solvent, as measured by the Gutmann Acceptor Number. Analogous SIEs for cations bear limited correlation with their radial charge density, highlighting a fundamental asymmetry in the origins of specific ion effects for anions and cations, due to competing non-Coulombic phenomena.

Thermochemical stabilities of giant fullerenes using density functional tight binding theory and isodesmic-type reactions.

We present a systematic assessment of the density functional tight binding (DFTB) method for calculating heats of formation of fullerenes with isodesmic-type reaction schemes. We show that DFTB3-D/3ob can accurately predict Δf H values of the 1812 structural isomers of C60 , reproduce subtle trends in Δf H values for 24 isolated pentagon rule (IPR) isomers of C84 , and predict Δf H values of giant fullerenes that are in effectively exact agreement with benchmark DSD-PBEP86/def2-QZVPP calculations. For fullerenes up to C320 , DFTB Δf H values are within 1.0 kJ mol-1 of DSD-PBEP86/def2-QZVPP values per carbon atom, and on a per carbon atom basis DFTB3-D/3ob yields exactly the same numerical trend of (Δf H [per carbon] = 722n-0.72 + 5.2 kJ mol-1 ). DFTB3-D/3ob is therefore an accurate replacement for high-level DHDFT and composite thermochemical methods in predicting of thermochemical stabilities of giant fullerenes and analogous nanocarbon architectures.

The Hubbard-U correction and optical properties of d0 metal oxide photocatalysts.

We report a systematic investigation of individual and multisite Hubbard-U corrections for the electronic, structural, and optical properties of the metal titanate oxide d0 photocatalysts SrTiO3 and rutile/anatase TiO2. Accurate bandgaps for these materials can be reproduced with local density approximation and generalized gradient approximation exchange-correlation density functionals via a continuous series of empirically derived Ud and Up combinations, which are relatively insensitive to the choice of functional. On the other hand, lattice parameters are much more sensitive to the choice of Ud and Up, but in a systematic way that enables the Ud and Up corrections to be used to qualitatively gauge the extent of self-interaction error in the electron density. Modest Ud corrections (e.g., 4 eV-5 eV) yield the most reliable dielectric response functions for SrTiO3 and are comparable to the range of Ud values derived via linear response approaches. For r-TiO2 and a-TiO2, however, the Ud,p corrections that yield accurate bandgaps fail to accurately describe both the parallel and perpendicular components of the dielectric response function. Analysis of individual Ud and Up corrections on the optical properties of SrTiO3 suggests that the most consequential of the two individual corrections is Ud, as it predominately determines the accuracy of the dominant excitation from O-2p to the Ti-3d t2g/eg orbitals. Up, on the other hand, can be used to shift the entire optical response uniformly to higher frequencies. These results will assist high-throughput and machine learning approaches to screening photoactive materials based on d0 photocatalysts.

Scalable growth of single-walled carbon nanotubes with a highly uniform structure.

Here, a scalable floating catalyst chemical vapor deposition (FCCVD) method is developed for the production of single-walled carbon nanotubes (SWCNTs) with a controlled structure. For the first time, water is used as the growth promoter in the FCCVD process to modulate the growth of SWCNTs. At an optimum water concentration of ca. 115 ppm, the water-assisted FCCVD process synthesizes SWCNTs with a significantly narrow chirality distribution. In particular, the proportion of (9,8) and (8,7) semiconducting tubes was dramatically enhanced to 45% with 27% of the (9,8) tube in the end product. This is attributed to the changes in both the SWCNT diameter and the chiral angle. The experiment results and accurate quantum chemical molecular dynamics simulations show that the addition of water affects the nucleation and the size distribution of nanoparticle catalysts, thus resulting in the growth of SWCNTs with a highly uniform structure. This direct and continuous water-assisted FCCVD provides the possibility for the mass production of high-quality SWCNTs with a controlled structure.