  Molecular dynamics (MD) simulations are increasingly being combined with
machine learning (ML) to predict material properties. The molecular
configurations obtained from MD are represented by multiple features, such as
thermodynamic properties, and are used as the ML input. However, to accurately
find the input--output patterns, ML requires a sufficiently sized dataset that
depends on the complexity of the ML model. Generating such a large dataset from
MD simulations is not ideal because of their high computation cost. In this
study, we present a simple supervised ML method to predict the transport
properties of materials. To simplify the model, an unsupervised ML method
obtains an efficient representation of molecular movements. This method was
applied to predict the viscosity of lubricant molecules in confinement with
shear flow. Furthermore, simplicity facilitates the interpretation of the model
to understand the molecular mechanics of viscosity. We revealed two types of
molecular mechanisms that contribute to low viscosity.
