  The motion of particles through density-stratified interfaces is a common
phenomenon in environmental and engineering applications. However, the
mechanics of particle-stratification interactions in various combinations of
particle and fluid properties are not well understood. This study presents a
novel machine-learning (ML) approach to experimental data of inertial particles
crossing a density-stratified interface. A simplified particle settling
experiment was conducted to obtain a large number of particles and expand the
parameter range, resulting in an unprecedented data set that has been shared as
open data. Using ML, the study explores new correlations that collapse the data
from this, and previous work Verso et al. (2019). The ``delay time,'' which is
the time between the particle exiting the interfacial layer and reaching a
steady-state velocity, is found to strongly depend on six dimensionless
parameters formulated by ML feature selection. The data shows a correlation
between the Reynolds and Froude numbers within the range of the experiments,
and the best symbolic regression is based on the Froude number only. This
experiment provides valuable insights into the behavior of inertial particles
in stratified layers and highlights opportunities for future improvement in
predicting their motion.
