  We have adapted a set of classification algorithms, also known as Machine
Learning, to the identification of fluid and gel domains close to the main
transition of dipalmitoyl-phosphatidylcholine (DPPC) bilayers. Using atomistic
molecular dynamics conformations in the low and high temperature phases as
learning sets, the algorithm was trained to categorize individual lipid
configurations as fluid or gel, in relation with the usual two-states
phenomenological description of the lipid melting transition. We demonstrate
that our machine can learn and sort lipids according to their most likely state
without prior assumption regarding the nature of the order parameter of the
transition. Results from our machine learning approach provides strong support
in favor of a two-states model approach of membrane fluidity.
