Liquid Densities, Kinematic Viscosities, and Heat Capacities of Some Alkylene Glycol Dialkyl Ethers

Esteve, Xavier; Conesa, and Albert; Coronas, Alberto

doi:10.1021/je025606c

Cited by 88 publications

(78 citation statements)

References 29 publications

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“…The water content, measured for each sample by Karl-Fisher titration, was always found to be less than 0.002 mass%. Further, the purities of liquids were checked by comparing the densities, viscosities, and speeds of sound at 298.15 ± 0.01 K with their corresponding literature values [18][19][20][21][22][23][24][25][26][27][28][29] in table 1.…”

Section: Methodsmentioning

confidence: 99%

Volumetric, acoustic, and viscometric studies of molecular interactions in binary mixtures of dipropylene glycol dimethyl ether with 1-alkanols at 298.15K

Pal

Gaba

2008

The Journal of Chemical Thermodynamics

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Section: Methodsmentioning

confidence: 99%

Volumetric, acoustic, and viscometric studies of molecular interactions in binary mixtures of dipropylene glycol dimethyl ether with 1-alkanols at 298.15K

Pal

Gaba

2008

The Journal of Chemical Thermodynamics

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“…The target observable was the liquid density, ρ, only, considered at four different temperatures and taken from [54]. The simulations performed were molecular dynamics simulations in the N pT ensemble executed with the software tool, GROMACS [46].…”

Section: Comparison To Gradient-based Methods Close To the Minimum: Dmentioning

confidence: 99%

SpaGrOW—A Derivative-Free Optimization Scheme for Intermolecular Force Field Parameters Based on Sparse Grid Methods

Hülsmann

Reith

2013

Entropy

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Molecular modeling is an important subdomain in the field of computational modeling, regarding both scientific and industrial applications. This is because computer simulations on a molecular level are a virtuous instrument to study the impact of microscopic on macroscopic phenomena. Accurate molecular models are indispensable for such simulations in order to predict physical target observables, like density, pressure, diffusion coefficients or energetic properties, quantitatively over a wide range of temperatures. Thereby, molecular interactions are described mathematically by force fields. The mathematical description includes parameters for both intramolecular and intermolecular interactions.While intramolecular force field parameters can be determined by quantum mechanics, the parameterization of the intermolecular part is often tedious. Recently, an empirical procedure, based on the minimization of a loss function between simulated and experimental physical properties, was published by the authors. Thereby, efficient gradient-based numerical optimization algorithms were used. However, empirical force field optimization is inhibited by the two following central issues appearing in molecular simulations: firstly, they are extremely time-consuming, even on modern and high-performance computer clusters, and secondly, simulation data is affected by statistical noise. The latter provokes the fact that an accurate computation of gradients or Hessians is nearly impossible close to a local or global minimum, mainly because the loss function is flat. Therefore, the question arises of whether to apply a derivative-free method approximating Entropy 2013, 15 3641 the loss function by an appropriate model function. In this paper, a new Sparse Grid-based Optimization Workflow (SpaGrOW) is presented, which accomplishes this task robustly and, at the same time, keeps the number of time-consuming simulations relatively small. This is achieved by an efficient sampling procedure for the approximation based on sparse grids, which is described in full detail: in order to counteract the fact that sparse grids are fully occupied on their boundaries, a mathematical transformation is applied to generate homogeneous Dirichlet boundary conditions. As the main drawback of sparse grids methods is the assumption that the function to be modeled exhibits certain smoothness properties, it has to be approximated by smooth functions first. Radial basis functions turned out to be very suitable to solve this task. The smoothing procedure and the subsequent interpolation on sparse grids are performed within sufficiently large compact trust regions of the parameter space. It is shown and explained how the combination of the three ingredients leads to a new efficient derivative-free algorithm, which has the additional advantage that it is capable of reducing the overall number of simulations by a factor of about two in comparison to gradient-based optimization methods. At the same time, the robustness with respect to statistical noise is...

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“…The densimeter is similar to those used by Sousa et al [21] and Esteve et al [22]. Its detailed description, together with construction and validation, can be found in the literature [23].…”

Section: Apparatusmentioning

confidence: 97%

P, ρ, T and heat capacity measurements of (α-pinene+β-pinene) mixtures over the temperature range 283.15K to 358.15K and pressures up to 40MPa: Experiments and modelling

Langa

Palavra

Lourenço

et al. 2013

The Journal of Chemical Thermodynamics

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Liquid Densities, Kinematic Viscosities, and Heat Capacities of Some Alkylene Glycol Dialkyl Ethers

Cited by 88 publications

References 29 publications

Volumetric, acoustic, and viscometric studies of molecular interactions in binary mixtures of dipropylene glycol dimethyl ether with 1-alkanols at 298.15K

Volumetric, acoustic, and viscometric studies of molecular interactions in binary mixtures of dipropylene glycol dimethyl ether with 1-alkanols at 298.15K

SpaGrOW—A Derivative-Free Optimization Scheme for Intermolecular Force Field Parameters Based on Sparse Grid Methods

P, ρ, T and heat capacity measurements of (α-pinene+β-pinene) mixtures over the temperature range 283.15K to 358.15K and pressures up to 40MPa: Experiments and modelling

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