Self-diffusion coefficients of amines, a molecular dynamics study

Castro-Anaya, Luis E.; Orozco, Gustavo A.

doi:10.1016/j.fluid.2021.113301

Cited by 14 publications

(4 citation statements)

References 38 publications

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“…We then ran the microcanonical NVE ensemble for a further 5 ns until the mean squared displacement data versus time entered a linear region, as recommended in previous literature . The nitrogen diffusivity was estimated to be 0.71 cm 2 /s at 300 K and atmospheric pressure in reasonable enough agreement with an experimental value of 0.212 cm 2 /s as simulated diffusivity could overestimate by 1–9 times of experimental value. − The FLARE++ model was able to capture an expected Arrhenius relation between diffusivity and temperature from our extended temperature range from 300 to 2000 K, as shown in Figure . Although diffusion coefficients above 1200 K (around 0.8 on the x -axis) deviate above the linear relation, our single transferable force field is still capable of capturing diffusivity in a gaseous phase over a very large range of temperature.…”

Section: Resultsmentioning

confidence: 58%

Transferable Force Field for Gallium Nitride Crystal Growth from the Melt Using On-The-Fly Active Learning

Chen,

Shao,

et al. 2023

J. Chem. Theory Comput.

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Atomic-scale simulations of reactive processes have been stymied by two factors: the lack of a suitable semiempirical force field on one hand and the impractically large computational burden of using ab initio molecular dynamics on the other hand. In this paper, we use an "on-the-fly" active learning technique to develop a nonparameterized force field that, in essence, exhibits the accuracy of density functional theory and the speed of a classical molecular dynamics simulation. We developed a force field capable of capturing the crystallization of gallium nitride (GaN) during a novel additive manufacturing process featuring the reaction of liquid Ga and gaseous nitrogen precursors to grow crystalline GaN thin films. We show that this machine learning model is capable of producing a single force field that can model solid, liquid, and gas phases involved in the process. We verified our computational predictions against a range of experimental measurements relevant to each phase and against ab initio calculations, showing that this nonparametric force field produces properties with excellent accuracy as well as exhibits computationally tractable efficiency. The force field is capable of allowing us to simulate the solid−liquid coexistence interface and the crystallization of GaN from the melt. The development of this transferable force field opens the opportunity to simulate the liquid-phase epitaxial growth more accurately than before to analyze reaction and diffusion processes and ultimately to establish a growth model of the additive manufacturing process to create the gallium nitride thin films.

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Section: Resultsmentioning

confidence: 58%

Transferable Force Field for Gallium Nitride Crystal Growth from the Melt Using On-The-Fly Active Learning

Chen,

Shao,

et al. 2023

J. Chem. Theory Comput.

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“…The calculations of the diffusion coefficients were obtained over the entire production time using the trajectory file and the defaults settings of the GROMACS utilities for the separation of time origins. From table 4 and figure 7 we can see that the diffusion coefficients calculated in this work have significant errors compared to the experiments [36]. Nevertheless, they have better agreement with them than the other force fields reported in the literature [36].…”

Section: -5supporting

confidence: 48%

“…From table 4 and figure 7 we can see that the diffusion coefficients calculated in this work have significant errors compared to the experiments [36]. Nevertheless, they have better agreement with them than the other force fields reported in the literature [36].…”

Section: -5supporting

confidence: 48%

Development of a new force field for the family of primary aliphatic amines using the three steps systematic parameterization procedure

Espinosa-Jiménez¹,

Salazar-Arriaga²,

Domínguez³

2023

Condens. Matter Phys.

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The applicability of the three steps systematic parametrization procedure (3SSPP) to develop a force field for primary amines was evaluated in the present work. Previous simulations of primary amines show that current force fields (FF) can underestimate some experimental values under room conditions. Therefore, we propose a new set of parameters, for an united atom (UA) model, that can be used for short and long amines which predict correctly thermodynamic and dynamical properties. Following the 3SSPP methodology, the partial charges are chosen to match the experimental dielectric constant whereas the Lennard-Jones (LJ) parameters, ε and σ, are fitted to reproduce the surface tension at the vapor-liquid interface and the liquid density, respectively. Simulations were initially conducted for the propylamine molecule by introducing three different types of carbon atoms, Cα and Cβ, with electric charges, and Cn, without charge. Then, modifying the charges of the carbons and using the transferable LJ parameters, the new set of constants for long amines were found. The results show good agreement for the experimental dielectric constant and mass density with a percentage error less than 1% surface tension the error is up to 4% ethylamine, the new charges were obtained from a fitting function calculated from the long amines results. For these molecules, the values of the dielectric constant and the surface tension present errors of the order of 10% with the experimental data. Miscibility of the amines was also tested with the new parameters and the results show reasonable agreement with experiments.

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“…The molecular dynamics (MD) simulation is also a common method for studying the self-diffusion of substances, where various properties of the system are calculated at the molecular scale. Castro-Anaya et al 19 reported the application of three different force fields, the optimized potential for liquid simulations of all atoms (OPLS-AA), transferable potentials for phase equilibria explicit hydrogens (TraPPE-EH), and a recent OPLS-AA (new-OPLS-AA) version, to calculate the self-diffusion coefficients of several amines. The new-OPLS-AA force field is a reparameterized version proposed by Dodda et al, 20 in which a scaling factor is used to account for the polarization effects.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Self-Diffusion Coefficient of Liquids Based on Backpropagation Artificial Neural Network: A Quantitative Structure–Property Relationship Study

Zeng

Ren

Xiao

et al. 2022

Ind. Eng. Chem. Res.

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The self-diffusion coefficient of pure liquids, a fundamental transport property, is involved in a wide range of applications. Many methods have been employed to study the self-diffusion coefficient, with the most popular being semiempirical models. The quantitative structure–property relationship (QSPR) has been widely used to predict various physicochemical properties of substances, but the appropriate molecular descriptors must be selected first. In this study, the charge density distribution area of molecules at a specific interval (S σi ) and cavity volume (V COSMO) was determined based on the conductor-like screening model for the segment activity coefficient (COSMO-SAC). Using these molecular descriptors, a backpropagation artificial neural network (BP-ANN) method was employed to construct a nonlinear QSPR model that can predict the self-diffusion coefficients of pure liquids under normal pressure. The data set used included 2596 data points for 238 compounds, covering a self-diffusion coefficient range of 8.74 × 10–13 to 8.66 × 10–9 m2·s–1 and a temperature range of 90.5–475.1 K. The coefficients of determination (R 2) of the BP-ANN model on the training, validation, and testing sets were all greater than 0.99. For the entire data set, the R 2, absolute average relative deviation (AARD), and root mean square error (RMSE) were 0.9940, 7.09%, and 0.1106, respectively. In an application domain (AD) analysis, 94.67% of the data were within the AD range of the model. Consequently, the model developed in this study can satisfactorily predict the self-diffusion coefficients of liquids.

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Self-diffusion coefficients of amines, a molecular dynamics study

Cited by 14 publications

References 38 publications

Transferable Force Field for Gallium Nitride Crystal Growth from the Melt Using On-The-Fly Active Learning

Transferable Force Field for Gallium Nitride Crystal Growth from the Melt Using On-The-Fly Active Learning

Development of a new force field for the family of primary aliphatic amines using the three steps systematic parameterization procedure

Predicting the Self-Diffusion Coefficient of Liquids Based on Backpropagation Artificial Neural Network: A Quantitative Structure–Property Relationship Study

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