Machine Learning Techniques for 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.

Automated summary

Recent advancements in simulations of fluid flows at the nanoscale involve massive data production and the use of machine learning techniques, allowing for insights into properties among molecular dynamics simulations and predictions of states not previously located. These developments offer an alternative approach for calculating transport properties of fluids and have the potential to enrich material property databases and enhance data-based scientific computations.