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Article

Machine Learning Techniques for Fluid Flows at the Nanoscale

Physics Department, University of Thessaly, 35100 Lamia, Greece
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Academic Editor: Laura A. Miller
Fluids 2021, 6(3), 96; https://doi.org/10.3390/fluids6030096
Received: 16 January 2021 / Revised: 15 February 2021 / Accepted: 23 February 2021 / Published: 1 March 2021
(This article belongs to the Special Issue Fluid Flows at the Nanoscale)
Simulations of fluid flows at the nanoscale feature massive data production and machine learning (ML) techniques have been developed during recent years to leverage them, presenting unique results. This work facilitates ML tools to provide an insight on properties among molecular dynamics (MD) simulations, covering missing data points and predicting states not previously located by the simulation. Taking the fluid flow of a simple Lennard-Jones liquid in nanoscale slits as a basis, ML regression-based algorithms are exploited to provide an alternative for the calculation of transport properties of fluids, e.g., the diffusion coefficient, shear viscosity and thermal conductivity and the average velocity across the nanochannels. Through appropriate training and testing, ML-predicted values can be extracted for various input variables, such as the geometrical characteristics of the slits, the interaction parameters between particles and the flow driving force. The proposed technique could act in parallel to simulation as a means of enriching the database of material properties, assisting in coupling between scales, and accelerating data-based scientific computations. View Full-Text
Keywords: machine learning; nanoflows; molecular dynamics; multivariate regression machine learning; nanoflows; molecular dynamics; multivariate regression
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MDPI and ACS Style

Sofos, F.; Karakasidis, T.E. Machine Learning Techniques for Fluid Flows at the Nanoscale. Fluids 2021, 6, 96. https://doi.org/10.3390/fluids6030096

AMA Style

Sofos F, Karakasidis TE. Machine Learning Techniques for Fluid Flows at the Nanoscale. Fluids. 2021; 6(3):96. https://doi.org/10.3390/fluids6030096

Chicago/Turabian Style

Sofos, Filippos; Karakasidis, Theodoros E. 2021. "Machine Learning Techniques for Fluid Flows at the Nanoscale" Fluids 6, no. 3: 96. https://doi.org/10.3390/fluids6030096

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