Powellwatkins1250

Ionic liquids (ILs) are salts, composed of asymmetric cations and anions, typically existing as liquids at ambient temperatures. They have found widespread applications in energy storage devices, dye-sensitized solar cells, and sensors because of their high ionic conductivity and inherent thermal stability. However, measuring the conductivity of ILs by physical methods is time-consuming and expensive, whereas the use of computational screening and testing methods can be rapid and effective. In this study, we used experimentally measured and published data to construct a deep neural network capable of making rapid and accurate predictions of the conductivity of ILs. The neural network is trained on 406 unique and chemically diverse ILs. This model is one of the most chemically diverse conductivity prediction models to date and improves on previous studies that are constrained by the availability of data, the environmental conditions, or the IL base. Feature engineering techniques were employed to identify key chemo-structural characteristics that correlate positively or negatively with the ionic conductivity. These features are capable of being used as guidelines to design and synthesize new highly conductive ILs. This work shows the potential for machine-learning models to accelerate the rate of identification and testing of tailored, high-conductivity ILs.In this paper, we consider the problem of quantifying parametric uncertainty in classical empirical interatomic potentials (IPs) using both Bayesian (Markov Chain Monte Carlo) and frequentist (profile likelihood) methods. We interface these tools with the Open Knowledgebase of Interatomic Models and study three models based on the Lennard-Jones, Morse, and Stillinger-Weber potentials. We confirm that IPs are typically sloppy, i.e., insensitive to coordinated changes in some parameter combinations. Because the inverse problem in such models is ill-conditioned, parameters are unidentifiable. This presents challenges for traditional statistical methods, as we demonstrate and interpret within both Bayesian and frequentist frameworks. We use information geometry to illuminate the underlying cause of this phenomenon and show that IPs have global properties similar to those of sloppy models from fields, such as systems biology, power systems, and critical phenomena. IPs correspond to bounded manifolds with a hierarchy of widths, leading to low effective dimensionality in the model. We show how information geometry can motivate new, natural parameterizations that improve the stability and interpretation of uncertainty quantification analysis and further suggest simplified, less-sloppy models.We report the ion transport mechanisms in succinonitrile (SN) loaded solid polymer electrolytes containing polyethylene oxide (PEO) and dissolved lithium bis(trifluoromethane)sulphonamide (LiTFSI) salt using molecular dynamics simulations. We investigated the effect of temperature and loading of SN on ion transport and relaxation phenomenon in PEO-LiTFSI electrolytes. It is observed that SN increases the ionic diffusivities in PEO-based solid polymer electrolytes and makes them suitable for battery applications. Interestingly, the diffusion coefficient of TFSI ions is an order of magnitude higher than the diffusion coefficient of lithium ions across the range of temperatures and loadings investigated. By analyzing different relaxation timescales and examining the underlying transport mechanisms in SN-loaded systems, we find that the diffusivity of TFSI ions correlates excellently with the Li-TFSI ion-pair relaxation timescales. In contrast, our simulations predict distinct transport mechanisms for Li-ions in SN-loaded PEO-LiTFSI electrolytes. Explicitly, the diffusivity of lithium ions cannot be uniquely determined by the ion-pair relaxation timescales but additionally depends on the polymer segmental dynamics. On the other hand, the SN loading induced diffusion coefficient at a given temperature does not correlate with either the ion-pair relaxation timescales or the polymer segmental relaxation timescales.Kohn-Sham density-functional theory (DFT), the predominant framework for electronic structure computations in chemistry today, has undergone considerable evolution in the past few decades. The earliest DFT approximations were based on uniform electron gas models completely free of empirical parameters. Tremendous improvements were made by incorporating density gradients and a small number of parameters, typically one or two, obtained from fits to atomic data. Incorporation of exact exchange and fitting to molecular data, such as experimental heats of formation, allowed even further improvements. This, however, opened a Pandora's Box of fitting possibilities, given the limitless choices of chemical reactions that can be fit. The result is a recent explosion of DFT approximations empirically fit to hundreds, or thousands, of chemical reference data. These fitted density functionals may contain several dozen empirical parameters. What has been lost in this fitting trend is physical modeling based on theory. In this work, we present a density functional comprising our best efforts to model exchange-correlation in DFT using good theory. We compare its performance to that of heavily fit density functionals using the GMTKN55 chemical reference data of Goerigk and co-workers [Phys. Chem. Chem. Phys. 19, 32184 (2017)]. Our density-functional theory, using only a handful of physically motivated pre-factors, competes with the best heavily fit Kohn-Sham functionals in the literature.We report and interpret recently recorded high-resolution infrared spectra for the fundamentals of the CH2 scissors and CH stretches of gas phase cyclopentane at -26.1 and -50 °C, respectively. We extend previous theoretical studies of this molecule, which is known to undergo barrierless pseudorotation due to ring puckering, by constructing local mode Hamiltonians of the stretching and scissor vibrations for which the frequencies, couplings, and linear dipoles are calculated as functions of the pseudorotation angle using B3LYP/6-311++(d,p) and MP2/cc-pVTZ levels of theory. Symmetrization (D5h) of the vibrational basis sets leads to simple vibration/pseudorotation Hamiltonians whose solutions lead to good agreement with the experiment at medium resolution, but which miss interesting line fractionation when compared to the high-resolution spectra. In contrast to the scissor motion, pseudorotation leads to significant state mixing of the CH stretches, which themselves are Fermi coupled to the scissor overtones.This work assesses the reliability of the recently proposed [M. Piris, Phys. Rev. Lett. 127, 233001 (2021)] global natural orbital functional (GNOF) in the treatment of the strong electron correlation regime. First, we use an H10 benchmark set of four hydrogen model systems of different dimensionalities and distinctive electronic structures a 1D chain, a 2D ring, a 2D sheet, and a 3D close-packed pyramid. Second, we study two paradigmatic models for strongly correlated Mott insulators, namely, a 1D H50 chain and a 4 × 4 × 4 3D H cube. We show that GNOF, without hybridization to other electronic structure methods and free of tuned parameters, succeeds in treating weak and strong correlation in a more balanced way than the functionals that have preceded it.Using a laser-induced local-heating experiment combined with temperature analysis, we observed the composition-dependent sign inversion of the Soret coefficient of SiO2 in binary silicate melts, which was successfully explained by a modified Kempers model used for describing the Soret effect in oxide melts. In particular, the diffusion of SiO2 to the cold side under a temperature gradient, which is an anomaly in silicate melts, was observed in the SiO2-poor compositions. The theoretical model indicates that the thermodynamic mixing properties of oxides, partial molar enthalpy of mixing, and partial molar volume are the dominant factors for determining the migration direction of the SiO2 component under a temperature gradient.Soret effect and diffusion in triethylene glycol (TEG)-water mixtures were investigated as a function of concentration at 25 °C by means of optical digital interferometry, with the use of a classical Soret cell. Diffusion D, thermal diffusion DT, and Soret ST coefficients are described for the full concentration range and an analysis is made individually for TEG-water mixture and within a series of n-ethylene glycol (n-EG) aqueous systems. All coefficients decrease with increasing the concentration of TEG and n-EG. ST shows a change of sign with concentration, and this change is directly related to the ability of the n-EG molecule to establish hydrogen bonding with water. Diffusion and thermal diffusion coefficients present a plateau behavior with increasing concentration, showing the occurrence of changes in the preferential interactions in aqueous solution with concentration and meaning that, at high TEG composition, ether oxygens can be involved in the molecular interactions.Besides absorbing light, the core antenna complex CP43 of photosystem II is of great importance in transferring excitation energy from the antenna complexes to the reaction center. Excitation energies, spectral densities, and linear absorption spectra of the complex have been evaluated by a multiscale approach. In this scheme, quantum mechanics/molecular mechanics molecular dynamics simulations are performed employing the parameterized density functional tight binding (DFTB) while the time-dependent long-range-corrected DFTB scheme is applied for the excited state calculations. The obtained average spectral density of the CP43 complex shows a very good agreement with experimental results. tetrathiomolybdate cost Moreover, the excitonic Hamiltonian of the system along with the computed site-dependent spectral densities was used to determine the linear absorption. While a Redfield-like approximation has severe shortcomings in dealing with the CP43 complex due to quasi-degenerate states, the non-Markovian full second-order cumulant expansion formalism is able to overcome the drawbacks. Linear absorption spectra were obtained, which show a good agreement with the experimental counterparts at different temperatures. This study once more emphasizes that by combining diverse techniques from the areas of molecular dynamics simulations, quantum chemistry, and open quantum systems, it is possible to obtain first-principle results for photosynthetic complexes, which are in accord with experimental findings.Despite decades of intense research, whether the transformation of supercooled liquids into glass is a kinetic phenomenon or a thermodynamic phase transition remains unknown. Here, we analyzed optical microscopy experiments on 2D binary colloidal glass-forming liquids and investigated the structural links of a prominent kinetic theory of glass transition. We examined a possible structural origin for localized excitations, which are building blocks of the dynamical facilitation theory-a purely kinetic approach for the glass transition. To accomplish this, we utilize machine learning methods to identify a structural order parameter termed "softness" that has been found to be correlated with reorganization events in supercooled liquids. Both excitations and softness qualitatively capture the dynamical slowdown on approaching the glass transition and motivated us to explore spatial and temporal correlations between them. Our results show that excitations predominantly occur in regions with high softness and the appearance of these high softness regions precedes excitations, thus suggesting a causal connection between them.