Extracting molecular mechanisms of shear-thinning of liquids at high strain rates using machine learning

Recent nonequilibrium molecular dynamics simulations have shown that shear-thinning of molecular liquids such as squalane at high strain rates (over 10^5 per second) exhibit a transition from a low-Newtonian-viscosity regime described well by power-law models to a high-Newtonian-viscosity regime where the flow properties are consistent with thermally-activated flow models. This talk explores the use of machine learning to probe the molecular origins of this rheological transition. Molecular trajectory data from simulations of small-molecular liquids sheared over a broad range of pressures and rates are used to design a 3D feature matrix. Using this matrix as input, several linear and nonlinear dimension reduction techniques are used to reduce the dimensionality of the feature space. We find that t-distributed stochastic neighbor embedding (t-SNE) can rapidly and effectively cluster trajectory data enabling the identification of molecular features (sets of atom pairs) diagnostic of the evolution in molecular order. Subsequent calculations of the order parameter and its linking with macroscopic rheological properties enable the determination of the amount of shear-thinning that comes from the evolution in order.