Mass effect on viscosity of mixtures in entropy scaling framework: Application to Lennard-Jones mixtures

Viet, Thieu Quang Quoc; Khennache, Samy; Galliéro, Guillaume; Alapati, Suresh; The, Nguyen Phuoc; Hoang, Hai

doi:10.1016/j.fluid.2022.113459

Cited by 6 publications

(2 citation statements)

References 49 publications

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“…In view of these attributes, our ML models were built using this data set. Here we note that there exists a significant amount of viscosity data of pure LJ systems [99,[102][103][104][105][106][107][108], and some sparse data of binary LJ systems [109][110][111] from other works. This collated data can, in principle, be used as a training, testing or constraining set in conjunction with the Vlugt data set.…”

Section: Vlugt Data Setmentioning

confidence: 96%

Building robust machine learning models for small chemical science data: the case of shear viscosity of fluids

Avula

Veesam

Balasubramanian

2022

Mach. Learn.: Sci. Technol.

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Shear viscosity, though being a fundamental property of all fluids, is computationally expensive to calculate from equilibrium molecular dynamics simulations. Recently, Machine Learning (ML) methods have been used to augment molecular simulations in many contexts, thus showing promise to estimate viscosity too in a relatively inexpensive manner. However, ML methods face significant challenges - such as overfitting, when the size of the data set is small, as is the case with viscosity. In this work, we train seven ML models to predict the shear viscosity of a Lennard-Jones (LJ) fluid, with particular emphasis on addressing issues arising from a small data set. Specifically, the issues related to model selection, performance estimation and uncertainty quantification were investigated. First, we show that the widely used performance estimation procedure of using a single unseen data set shows a wide variability small data sets. In this context, the common practice of using Cross validation (CV) to select the hyperparameters (model selection) can be adapted to estimate the generalization error (performance estimation) as well. We compare two simple CV procedures for their ability to do both model selection and performance estimation, and find that k-fold CV based procedure shows a lower variance of error estimates. We discuss the role of performance metrics in training and evaluation and propose a method to rank the ML models based on multiple metrics. Finally, two methods for uncertainty quantification - Gaussian Process Regression (GPR) and ensemble method - were used to estimate the uncertainty on individual predictions. The uncertainty estimates from GPR were also used to construct an applicability domain using which the ML models provided even more reliable predictions on an independent viscosity data set generated in this work. Overall, the procedures prescribed in this work, together, lead to robust ML models for small data sets.

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Section: Vlugt Data Setmentioning

confidence: 96%

Building robust machine learning models for small chemical science data: the case of shear viscosity of fluids

Avula

Veesam

Balasubramanian

2022

Mach. Learn.: Sci. Technol.

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“…In future work, it would be interesting to explore how the above discussion on the microscopic origin of the Arrhenius law observed for a LJ liquid extends to more complex situations, and in particular to real liquids of interest such as water. Additionally, exploring the connection between the rather simple free volume model, and alternative microscopic descriptions such as excess entropy scaling [31][32][33][34][35] could provide additional insights on the emergence of a simple Arrhenius behaviour without the need for activated processes. Finally, rationalizing the deviations from the Arrhenius behaviour typical of fragile supercooled liquids [36][37][38], where thermal energy can become lower than activation energies, could require to go one step further and combine both free volume and activation models [39].…”

mentioning

confidence: 99%

Microscopic origins of the viscosity of a Lennard-Jones liquid

Rizk,

Gelin,

Biance

et al. 2022

Preprint

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Symbolic regression development of empirical equations for diffusion in Lennard-Jones fluids

Alam

Allers

Leverant

et al. 2022

The Journal of Chemical Physics

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Symbolic regression (SR) with a multi-gene genetic program has been used to elucidate new empirical equations describing diffusion in Lennard-Jones (LJ) fluids. Examples include equations to predict self-diffusion in pure LJ fluids and equations describing the finite-size correction for self-diffusion in binary LJ fluids. The performance of the SR-obtained equations was compared to that of both the existing empirical equations in the literature and to the results from artificial neural net (ANN) models recently reported. It is found that the SR equations have improved predictive performance in comparison to the existing empirical equations, even though employing a smaller number of adjustable parameters, but show an overall reduced performance in comparison to more extensive ANNs.

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Mass effect on viscosity of mixtures in entropy scaling framework: Application to Lennard-Jones mixtures

Cited by 6 publications

References 49 publications

Building robust machine learning models for small chemical science data: the case of shear viscosity of fluids

Building robust machine learning models for small chemical science data: the case of shear viscosity of fluids

Microscopic origins of the viscosity of a Lennard-Jones liquid

Symbolic regression development of empirical equations for diffusion in Lennard-Jones fluids

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