Linking Rheological Properties with Molecular-Scale Features via Molecular Dynamics Simulations and Machine Learning

Accurate molecular-scale understanding of rheological properties of small-molecular liquids and polymers is critical to optimizing their performance in practical applications such as lubrication and hydraulic fracking. Non-equilibrium molecular dynamics (NEMD) simulations can extract the flow behavior of materials sheared at very high strain rates that are difficult to reach in experiments. However, NEMD simulations produce a large amount of high-dimensional output data, which is often under-utilized in examining the link between molecular-scale features and rheological properties. We combine NEMD simulations with machine learning (ML) methods such as principal component analysis and t-distributed stochastic neighbor embedding to extract the correlation between molecular structure and shear thinning of small-molecular liquids such as squalane, polydecene trimer, and 9-octylheptadecane over a broad range of strain rates (105 - 1010 s-1), pressures (0.1 - 1000 MPa), and temperatures (293 - 373 K). ML reveals the competing contributions of end-to-end atom pairs and side branches toward shear thinning of fluids with low Newtonian viscosities. Data-driven insights into the molecular-scale origins of shear thinning of fluids with high Newtonian viscosity are also presented.