  We use machine learning algorithms to detect the crystalline phase in
undercooled melts in molecular dynamics simulations. Our classification method
is based on local conformation and environmental fingerprints of individual
monomers. In particular, we employ self-supervised auto-encoders to compress
the fingerprint information and a Gaussian mixture model to distinguish ordered
states from disordered ones. The resulting identification of crystalline
monomers agrees to a large extent with human-defined classifiers such as the
stem-length-based classification scheme as developed in our previous work [C.
Luo and J.-U. Sommer, Macromolecules 44 (2011), 1523], but does not require any
foreknowledge about the structure of semi-crystalline polymers. Because of its
local sensitivity, the method allows the resolution of detailed time patterns
of crystalline order before an apparent signature of the transition is visible
in thermodynamic properties such as for the specific volume. At a
pre-transition point, we observe the highest crystallization efficiency using
the fraction of monomers being conserved in the crystalline phase as compared
to the number of monomers joining that phase.
