  Understanding epitaxial strain relaxation is one of the key challenges in
functional thin films with strong structure-property relation. Herein, we
employ an emerging data analytics approach to quantitatively evaluate the
underlying relationships between critical thickness (hc) of strain relaxation
and various physical and chemical features, despite the sporadic experimental
data points available. First, we have collected and refined reported hc of
perovskite oxide thin film/substrate system to construct a consistent
sub-dataset which captures a common trend among the varying experimental
details. Then, we employ correlation analyses and feature engineering to find
the most relevant feature set which include Poisson's ratio and lattice
mismatch. With the insight offered by correlation analyses and feature
engineering, machine learning (ML) models have been trained to deduce a decent
accuracy, which has been further validated experimentally. the demonstrated
framework is expected to be efficiently extended to the other classes of thin
films in understanding hc.
