Bridging the size gap between experiment and theory: large-scale DFT calculations on realistic sized Pd particles for acetylene hydrogenation.

Metal nanoparticles, often supported on metal oxide promoters, are a cornerstone of heterogeneous catalysis. Experimentally, size effects are well-established and are manifested through changes to catalyst selectivity, activity and durability. Density Functional Theory (DFT) calculations have provided an attractive way to study these effects and rationalise the change in nanoparticle properties. However such computational studies are typically limited to smaller nanoparticles (approximately up to 50 atoms) due to the large computational cost of DFT. How well can such simulations describe the electronic properties of the much larger nanoparticles that are often used in practice? In this study, we use the ONETEP code, which is able to achieve more favourable computational scaling for metallic nanoparticles, to bridge this size gap. We present DFT calculations on entire Pd and Pd carbide nanoparticles of more than 300 atoms (approximately 2.5 nm diameter), and find major differences in the electronic structure of such large nanoparticles, in comparison to the commonly investigated smaller clusters. These differences are also manifested in the calculated chemical properties such as adsorption energies for C2H2, C2H4 and C2H6 on the pristine Pd and PdC x nanoparticles which are significantly larger (up to twice in value) for the ∼300 atoms structures. Furthermore, the adsorption of C2H2 and C2H4 on PdC x nanoparticles becomes weaker as more C is introduced in the Pd lattice whilst the impact of C concentration is also observed in the calculated reaction energies towards the hydrogenation of C2H2, where the formation of C2H6 is hindered. Our simulations show that PdC x nanoparticles of about 5% C per atom fraction and diameter of 2.5 nm could be potential candidate catalysts of high activity in hydrogenation reactions. The paradigm presented in this study will enable DFT to be applied on similar sized metal catalyst nanoparticles as in experimental investigations, strengthening the synergy between simulation and experiment in catalysis.

Conformational Analysis of 1,3-Difluorinated Alkanes.

Fluorine substitution can have a profound impact on molecular conformation. Here, we present a detailed conformational analysis of how the 1,3-difluoropropylene motif (-CHF-CH2-CHF-) determines the conformational profiles of 1,3-difluoropropane, anti- and syn-2,4-difluoropentane, and anti- and syn-3,5-difluoroheptane. It is shown that the 1,3-difluoropropylene motif strongly influences alkane chain conformation, with a significant dependence on the polarity of the medium. The conformational effect of 1,3-fluorination is magnified upon chain extension, which contrasts with vicinal difluorination. Experimental evidence was obtained from NMR analysis, where polynomial complexity scaling simulation algorithms were necessary to enable J-coupling extraction from the strong second-order spectra, particularly for the large 16-spin systems of the difluorinated heptanes. These results improve our understanding of the conformational control toolkit for aliphatic chains, yield simple rules for conformation population analysis, and demonstrate quantum mechanical time-domain NMR simulations for liquid state systems with large numbers of strongly coupled spins.

An Electronic Structure Investigation of PEDOT with AlCl4- Anions-A Promising Redox Combination for Energy Storage Applications.

In this work, we use density functional theory to investigate the electronic structure of poly(3,4-ethylenedioxythiophene) (PEDOT) oligomers with co-located AlCl4- anions, a promising combination for energy storage. The 1980s bipolaron model remains the dominant interpretation of the electronic structure of PEDOT despite recent theoretical progress that has provided new definitions of bipolarons and polarons. By considering the influence of oligomer length, oxidation or anion concentration and spin state, we find no evidence for many of the assertions of the 1980s bipolaron model and so further contribute to a new understanding. No self-localisation of positive charges in PEDOT is found, as predicted by the bipolaron model at the hybrid functional level. Instead, our results show distortions that exhibit a single or a double peak in bond length alternations and charge density. Either can occur at different oxidation or anion concentrations. Rather than representing bipolarons or polaron pairs in the original model, these are electron distributions driven by a range of factors. Distortions can span an arbitrary number of nearby anions. We also contribute a novel conductivity hypothesis. Conductivity in conducting polymers has been observed to reduce at anion concentrations above 0.5. We show that at high anion concentrations, the energy of the localised, non-bonding anionic orbitals approaches that of the system HOMO due to Coulombic repulsion between anions. We hypothesize that with nucleic motion in the macropolymer, these orbitals will interfere with the hopping of charge carriers between sites of similar energy, lowering conductivity.

A Workflow for Identifying Viable Crystal Structures with Partially Occupied Sites Applied to the Solid Electrolyte Cubic Li7La3Zr2O12.

To date, experimental and theoretical works have been unable to uncover the ground-state configuration of the solid electrolyte cubic Li7La3Zr2O12 (c-LLZO). Computational studies rely on an initial low-energy structure as a reference point. Here, we present a methodology for identifying energetically favorable configurations of c-LLZO for a crystallographically predicted structure. We begin by eliminating structures that involve overlapping Li atoms based on nearest neighbor counts. We further reduce the configuration space by eliminating symmetry images from all remaining structures. Then, we perform a machine learning-based energetic ordering of all remaining structures. By considering the geometrical constraints that emerge from this methodology, we determine that a large portion of previously reported structures may not be feasible or stable. The method developed here could be extended to other ion conductors. We provide a database containing all of the generated structures with the aim of improving accuracy and reproducibility in future c-LLZO research.

Calculating shear viscosity with confined non-equilibrium molecular dynamics: a case study on hematite - PAO-2 lubricant.

The behaviour of confined lubricants at the atomic scale as affected by the interactions at the surface-lubricant interface is relevant in a range of technological applications in areas such as the automotive industry. In this paper, by performing fully atomistic molecular dynamics, we investigate the regime where the viscosity starts to deviate from the bulk behaviour, a topic of great practical and scientific relevance. The simulations consist of setting up a shear flow by confining the lubricant between iron oxide surfaces. By using confined Non-Equilibrium Molecular Dynamics (NEMD) simulations at a pressure range of 0.1-1.0 GPa at 100 °C, we demonstrate that the film thickness of the fluid affects the behaviour of viscosity. We find that by increasing the number of lubricant molecules, we approach the viscosity value of the bulk fluid derived from previously published NEMD simulations for the same system. These changes in viscosity occurred at film thicknesses ranging from 10.12 to 55.93 Å. The viscosity deviations at different pressures between the system with the greatest number of lubricant molecules and the bulk simulations varied from -16% to 41%. The choice of the utilized force field for treating the atomic interactions was also investigated.

Does a Machine-Learned Potential Perform Better Than an Optimally Tuned Traditional Force Field? A Case Study on Fluorohydrins.

We present a comparative study that evaluates the performance of a machine learning potential (ANI-2x), a conventional force field (GAFF), and an optimally tuned GAFF-like force field in the modeling of a set of 10 γ-fluorohydrins that exhibit a complex interplay between intra- and intermolecular interactions in determining conformer stability. To benchmark the performance of each molecular model, we evaluated their energetic, geometric, and sampling accuracies relative to quantum-mechanical data. This benchmark involved conformational analysis both in the gas phase and chloroform solution. We also assessed the performance of the aforementioned molecular models in estimating nuclear spin-spin coupling constants by comparing their predictions to experimental data available in chloroform. The results and discussion presented in this study demonstrate that ANI-2x tends to predict stronger-than-expected hydrogen bonding and overstabilize global minima and shows problems related to inadequate description of dispersion interactions. Furthermore, while ANI-2x is a viable model for modeling in the gas phase, conventional force fields still play an important role, especially for condensed-phase simulations. Overall, this study highlights the strengths and weaknesses of each model, providing guidelines for the use and future development of force fields and machine learning potentials.

Computing Viscosities of Mixtures of Ester-Based Lubricants at Different Temperatures.

Synthetic esters are used as lubricants for applications at high temperatures, but their development can be a trial and error process. In this context, molecular dynamics simulations could be used as a tool to investigate the properties of new lubricants, in particular viscosity. We employ nonequilibrium molecular dynamics (NEMD) simulations to predict bulk Newtonian viscosities of a set of mixtures of two esters, di(2-ethylhexyl) sebacate (DEHS) and di(2-ethylhexyl) adipate (DEHA) at 293 and 343 K as well as equilibrium molecular dynamics (EMD) and NEMD at 393 K and compare these to experimental measurements. The simulations predict mixture densities within 5% of the experimental values, and we are able to retrieve between 99% and 75% of the experimental viscosities for all ranges of temperature. Experimental viscosities show a linear trend which we are able to capture using NEMD at low temperature and EMD at high temperature. Our work shows that, using EMD and NEMD simulations, and the workflows we developed, we can obtain reliable estimates of the viscosities of mixtures of industrially relevant ester-based lubricants at different temperatures.

Atomistic simulations on the carbidisation processes in Pd nanoparticles.

The formation of interstitial PdC x nanoparticles (NPs) is investigated through DFT calculations. Insights on the mechanisms of carbidisation are obtained whilst the material's behaviour under conditions of increasing C-concentration is examined. Incorporation of C atoms in the Pd octahedral interstitial sites is occurring through the [111] facet with an activation energy barrier of 19.3-35.7 kJ mol-1 whilst migration through the [100] facet corresponds to higher activation energy barriers of 124.5-127.4 kJ mol-1. Furthermore, interstitial-type diffusion shows that C will preferentially migrate and reside at the octahedral interstitial sites in the subsurface region with limited mobility towards the core of the NP. For low C-concentrations, migration from the surface into the interstitial sites of the NPs is thermodynamically favored, resulting in the formation of interstitial carbide. Carbidisation reaction energies are exothermic up to 11-14% of C-concentration and slightly vary depending on the shape of the structure. The reaction mechanisms turn to endothermic for higher concentration levels showing that C will preferentially reside on the surface making the interstitial carbide formation unfavorable. As experimentally observed, our simulations confirm that there is a maximum concentration of C in Pd carbide NPs opening the way for further computational investigations on the activity of Pd carbides in directed catalysis.

Using molecular dynamics to simulate realistic structures of nitrocellulose of different nitration levels.

Nitrocellulose is a reactive derivative of cellulose, one of the most commonly occurring natural materials. Nitration of cellulose decreases the stability of the structure, meaning less is understood about its structure and reactions. Although cellulose is often found in fully crystalline forms, nitrocellulose is more commonly paracrystalline, or amorphous. We present a protocol based on molecular dynamics simulations for creating realistic structures of nitrocellulose, particularly focusing on the crystallinity of the systems being created. We will also provide a detailed analysis of the geometric and dynamical parameters used to quantify the degree of crystallinity for the structures created here, with nitration levels varying from 0-14.14 wt% nitrogen content. Paracrystalline cellulose was not created using the protocol designed here, although it was found that the more nitrated a nitrocellulose system, the more the structure tends to paracrystallinity. This is due to a decrease in the number of hydrogen bonds present, and an increase in the size of the functional groups pushing the chains apart and weakening the interactions between the chains of the structure. The structures created are representative of realistic systems, which in the future will be able to be used to build further understanding of long-term storage of nitrocellulose.

BRD4: quantum mechanical protein-ligand binding free energies using the full-protein DFT-based QM-PBSA method.

Fully quantum mechanical approaches to calculating protein-ligand free energies of binding have the potential to reduce empiricism and explicitly account for all physical interactions responsible for protein-ligand binding. In this study, we show a realistic test of the linear-scaling DFT-based QM-PBSA method to estimate quantum mechanical protein-ligand binding free energies for a set of ligands binding to the pharmaceutical drug-target bromodomain containing protein 4 (BRD4). We show that quantum mechanical QM-PBSA is a significant improvement over traditional MM-PBSA in terms of accuracy against experiment and ligand rank ordering and that the quantum and classical binding energies are converged to a similar degree. We test the interaction entropy and normal mode entropy correction terms to QM- and MM-PBSA.