Molecular Modeling of Poly(Ethylene Oxide) Melts and Poly(Ethylene Oxide)-Based Polymer Electrolytes

The behavior of water near a graphene sheet is investigated by means of ab initio and classical molecular dynamics simulations. The wetting of the graphene sheet by ab initio water and the relation of such behavior to the strength of classical dispersion interaction between surface atoms and water are explored. The first principles simulations reveal a layered solvation structure around the graphene sheet with a significant water density in the interfacial region implying no drying or cavitation effect. It is found that the ab initio results of water density at interfaces can be reproduced reasonably well by classical simulations with a tuned dispersion potential between the surface and water molecules. Calculations of vibrational power spectrum from ab initio simulations reveal a shift of the intramolecular stretch modes to higher frequencies for interfacial water molecules when compared with those of the second solvation later or bulk-like water due to the presence of free OH modes near the graphene sheet. Also, a weakening of the water-water hydrogen bonds in the vicinity of the graphene surface is found in our ab initio simulations as reflected in the shift of intermolecular vibrational modes to lower frequencies for interfacial water molecules. The first principles calculations also reveal that the residence and orientational dynamics of interfacial water are somewhat slower than those of the second layer or bulk-like molecules. However, the lateral diffusion and hydrogen bond relaxation of interfacial water molecules are found to occur at a somewhat faster rate than that of the bulk-like water molecules. The classical molecular dynamics simulations with tuned Lennard-Jones surface-water interaction are found to produce dynamical results that are qualitatively similar to those of ab initio molecular dynamics simulations.

We have performed extensive ab initio and classical molecular dynamics (MD) simulations of benzene in water in order to examine the unique solvation structures that are formed. Qualitative differences between classical and ab initio MD simulations are found and the importance of various technical simulation parameters is examined. Our comparison indicates that nonpolarizable classical models are not capable of describing the solute-water interface correctly if local interactions become energetically comparable to water hydrogen bonds. In addition, a comparison is made between a rigid water model and fully flexible water within ab initio MD simulations which shows that both models agree qualitatively for this challenging system.

Using classical and ab initio molecular-dynamics (MD) simulations, we have studied a calcium aluminosilicate glass of composition (SiO2)0.67–(Al2O3)0.12–(CaO)0.21. Samples with 100 and 200 atoms were generated by classical MD simulations using a potential with 3-body interactions. Although we observe, for the model with 100 atoms, finite size effects for some structural properties, these effects are substantially reduced if the glass structure is refined by the ab initio MD simulations. In addition, some structural characteristics such as the Ca–O bond length and the angular distributions are improved by the ab initio description. The structural and vibrational characteristics of these glass samples are compared to that of a glass that has been quenched from the melt using first-principles simulations. The main differences are found on the SiOSi and SiOAl angular distributions and on the apparition of high-frequency bands in the partial Ca vibrational density of states for the classically generated glass samples.

Establishing a unified framework for ion solvation and transport in liquid and solid electrolytes

The shock Hugoniot for full-density and porous ${\mathrm{CeO}}_{2}$ was investigated in the liquid regime using ab initio molecular dynamics (AIMD) simulations with Erpenbeck's approach based on the Rankine-Hugoniot jump conditions. The phase space was sampled by carrying out NVT simulations for isotherms between 6000 and 100 000 K and densities ranging from $\ensuremath{\rho}=2.5$ to $20\phantom{\rule{0.28em}{0ex}}\mathrm{g}/{\mathrm{cm}}^{3}$. The impact of on-site Coulomb interaction corrections $+U$ on the equation of state (EOS) obtained from AIMD simulations was assessed by direct comparison with results from standard density functional theory simulations. Classical molecular dynamics (CMD) simulations were also performed to model atomic-scale shock compression of larger porous ${\mathrm{CeO}}_{2}$ models. Results from AIMD and CMD compression simulations compare favorably with Z-machine shock data to 525 GPa and gas-gun data to 109 GPa for porous ${\mathrm{CeO}}_{2}$ samples. Using results from AIMD simulations, an accurate liquid-regime Mie-Gr\"uneisen EOS was built for ${\mathrm{CeO}}_{2}$. In addition, a revised multiphase SESAME-type EOS was constrained using AIMD results and experimental data generated in this work. This study demonstrates the necessity of acquiring data in the porous regime to increase the reliability of existing analytical EOS models.

Ab initio and classical molecular dynamics studies of electrode reactions

Distribution of the mechanical properties of Ti–Cu combinatorial thin film evaluated using nanoindentation experiments and molecular dynamics with a neural network potential

Eliminating organic pollutant molecules in water is a world challenge, that non-thermal plasmas at atmospheric pressure are aiming to address. Non thermal plasmas are used for treating contaminated water due to their ability to produce reactive oxygen and nitrogen species, the so-called RONS, which diffuse into water, react with pollutant molecules, and degrade them. Many experimental works have been designed, using different plasma sources [1, 2], but in all cases HO● radicals are acknowledged to play the main role in the degradation process.Classical and ab-initio reactive molecular dynamics (MD) simulations are relevant techniques for describing interactions of HO● with pollutant molecules in water, leading to the knowledge of degradation products, chemical pathways and rates [3]. Basically, MD simulations are solving the Newton equations of motion of a set of species, say atoms, molecules, clusters, etc. It is able to describe system size up to billion of species. It only needs the knowledge of interactions (force fields) between all species as well as initial conditions, preferably consistent with experiments. So it is suitable for addressing chemical reactivity. Classical reactive MD simulations are using available semi-empirical force fields, such as reaxFF, while ab-initio MD are required when no such force field is available. In this case, force fields are calculated quantum mechanically at relevant time steps of MD simulations.In fact, MD simulations act as a predictive "virtual microscope" provided the force fields are enough precise.In this work, MD simulations of the interaction of HO● radicals with molecules representative of various emerging pollutant families, as pesticide, antibiotics, PFAS in water, are studied. For mimicking HO● radicals release into water by non-thermal plasma, HO● radicals are periodically injected into a simulation box containing a pollutant molecule surrounded by several tens of water molecules. For obtaining statistically significant results, at least 10 simulations, with different initial conditions are run. The products that are formed will be compared with experiments, especially HPLC, GCMS and MS/MS measurements of plasma treated water.Another approach, consists in introducing a temperature ramp during the simulation for determining at which temperature products are appearing and in which order. Such an approach is revealing the reaction barriers up to full decomposition of the pollutant molecule. It can thus, by back-analysis, help to define which plasma or other advanced oxidation processing will be relevant [3]. Acknowledgements Part of this work has been supported by Conseil Régional Centre-Val de Loire with grant #2021-00144786 for projectPerturb’Eau. Monica Magureanu, Corina Bradu, Florin Bilea, Olivier Aubry, Dunpin Hong, Manoj Panyamthatta-Tayaroth are gratefully acknowledged for stimulating discussions and for providing experimental results.

Results of ab initio molecular dynamics (AIMD), quantum mechanics/molecular mechanics (QM/MM), and classical molecular dynamics (CMD) simulations of Cm(3+) in liquid water at a temperature of 300 K are reported. The AIMD simulation was based on the Car-Parrinello MD scheme and GGA-PBE formulation of density functional theory. Two QM/MM simulations were performed by treating Cm(3+) and the water molecules in the first shell quantum mechanically using the PBE (QM/MM-PBE) and the hybrid PBE0 density functionals (QM/MM-PBE0). Two CMD simulations were carried out using ab initio derived pair plus three-body potentials (CMD-3B) and empirical Lennard-Jones pair potential (CMD-LJ). The AIMD and QM/MM-PBE simulations predict average first shell hydration numbers of 8, both of which disagree with recent experimental EXAFS and TRLFS value of 9. On the other hand, the average first shell hydration numbers obtained in the QM/MM-PBE0 and CMD simulations was 9, which agrees with experiment. All the simulations predicted an average first shell and second shell Cm-O bond distance of 2.49-2.53 Å and 4.67-4.75 Å respectively, both of which are in fair agreement with corresponding experimental values of 2.45-2.48 and 4.65 Å. The geometric arrangement of the 8-fold and 9-fold coordinated first shell structures corresponded to the square antiprism and tricapped trigonal prisms, respectively. The second shell hydration number for AIMD QM/MM-PBE, QM/MM-PBE0, CMD-3B, and CMD-LJ, were 15.8, 17.2, 17.7, 17.4, and 16.4 respectively, which indicates second hydration shell overcoordination compared to a recent EXAFS experimental value of 13. Save the EXAFS spectra CMD-LJ simulation, all the computed EXAFS spectra agree fairly well with experiment and a clear distinction could not be made between configurations with 8-fold and 9-fold coordinated first shells. The mechanisms responsible for the first shell associative and dissociative ligand exchange in the classical simulations have been analyzed. The first shell mean residence time was predicted to be on the nanosecond time scale. The computed diffusion constants of Cm(3+) and water are in good agreement with experimental data.

We present the absolute enthalpy, entropy, heat capacity, and free energy of liquid water at ambient conditions calculated by the two-phase thermodynamic method applied to ab initio, reactive and classical molecular dynamics simulations. We find that the absolute entropy and heat capacity of liquid water from ab initio molecular dynamics (AIMD) is underestimated, but falls within the range of the flexible empirical as well as the reactive force fields. The origin of the low absolute entropy of liquid water from AIMD simulations is due to an underestimation of the translational entropy by 20% and the rotational entropy by 40% compared to the TIP3P classical water model, consistent with previous studies that reports low diffusivity and increased ordering of liquid water from AIMD simulations. Classical MD simulations with rigid water models tend to be in better agreement with experiment (in particular TIP3P yielding the best agreement), although the TIP4P-ice water model, the only empirical force field that reproduces the experimental melting temperature, has the lowest entropy, perhaps expectedly. This reiterates the limitations of existing empirical water models in simultaneously capturing the thermodynamics of solid and liquid phases. We find that the quantum corrections to heat capacity of water can be as large as 60%. Although certain water models are computed to yield good absolute free energies of water compared to experiments, they are often due to the fortuitous enthalpy-entropy cancellation, but not necessarily due to the correct descriptions of enthalpy and entropy separately.

Structure and dynamics of B2O3 melts and glasses: From ab initio to classical molecular dynamics simulations

Graphene has been one of the most researched material in the world for the past two decades due to its unique combination of mechanical, thermal and electrical properties. Graphene exists in a stable two dimensional (2D) structure with hexagonal carbon rings. This special 2D structure of graphene enables it to exhibit a wide range of peculiar material properties like high Young’s modulus, high specific strength, and electrical conductivity etc. However, it is extremely challenging and costly to investigate graphene solely based on experimental tests. Atomistic simulations are powerful computational techniques for characterizing materials at small length and time scales with a fraction of cost relative to experimental testing. High fidelity atomistic simulations like Density Functional Theory (DFT) simulations, and ab initio molecular dynamic simulations have higher accuracy in predicting 2D material properties but are computationally expensive. Classic molecular dynamics (MD) simulations adopt empirical interatomic potentials which drastically reduce the computational time but has lower simulation accuracy. To bridge the gap between these two type of simulation techniques, a new artificial neural network potential is developed, for graphene in this study, to enable the characterization of 2D materials using classic MD simulations with a comparable accuracy of first principles simulation. This is expected to accelerate the discovery and design of novel graphene based functional materials. In the present study mechanical and thermal properties of graphene are investigated using the machine learning potentials by conducting MD simulations. To validate the accuracy of machine learning potentials mechanical properties such as Young’s modulus, ultimate tensile strength and thermal properties such as coefficient of thermal expansion and lattice parameter are evaluated for graphene and compared with existing literature.

Water structures on Pb(100) and (111) surface studied with the Interface force field

Combining theoretical and experimental approaches, we investigate the solvation properties of Li+ ions in a series of ether solvents (dimethoxyethane, diglyme, triglyme, tetraglyme, and 15-crown-5) and their subsequent effects on the solid-state lithium-sulfur reactions in subnano confinement. The ab initio and classical molecular dynamics (MD) simulations predict Li+ ion solvation structures within ether solvents in excellent agreement with experimental evidence from electrospray ionization-mass spectroscopy. An excellent correlation is also established between the Li+-solvation binding energies from the ab initio MD simulations and the lithiation overpotentials obtained from galvanostatic intermittent titration techniques (GITT). These findings convincingly indicate that a stronger solvation binding energy imposes a higher lithiation overpotential of sulfur in subnano confinement. The mechanistic understanding achieved at the electronic and atomistic level of how Li+-solvation dictates its electrochemical reactions with sulfur in subnano confinement provides invaluable guidance in designing future electrolytes and electrodes for Li-sulfur chemistry.

Lithium thiophosphate electrolyte is a promising material for application in all-solid-state batteries. Ab initio molecular dynamics (AIMD) simulations have been used to investigate the ion conduction mechanisms in single-crystalline and glassy compounds. However, the complexity of real materials (e.g., materials with grain boundaries and multiphase glass-ceramics) causes AIMD simulations to have high computational cost. To overcome this computational limitation, we developed a new interatomic potential for classical molecular dynamics (CMD) simulations of Li solid-state electrolytes. The training datasets were generated from representative sulfide electrolytes (β-Li3PS4, γ-Li3PS4, Li4P2S6, Li7P3S11, and Li7PS6 crystals and 70Li2S-30P2S5 glass). Using the functional forms of the Class II and Stillinger-Weber potentials, all parameters were optimized by minimizing the differences in forces on atoms, stresses, and potential energies between the CMD and AIMD results. Subsequent validation showed that the optimized parameters can reproduce the dynamics of Li+ as well as the structures of the crystalline and glassy materials. The ionic conductivity of Li7P3S11 crystal was approximately five times that of the isostoichiometric 70Li2S-30P2S5 glass, indicating that CMD simulations using the developed force-field accurately reproduced the effective conduction path in Li7P3S11 from AIMD. The developed force-field parameters make it possible to simulate complex materials including amorphous-crystalline interfaces and multiphase glass-ceramics in the CMD framework.