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CPP: Fachverband Chemische Physik und Polymerphysik
CPP 91: Computational Biophysics (joint session BP/CPP)
CPP 91.8: Vortrag
Donnerstag, 19. März 2020, 12:00–12:15, SCH A251
A machine learning assessment of the two states model for lipid bilayer phase transitions — •Vivien Walter1, Céline Ruscher2, Olivier Benzerara2, Carlos Marques2, and Fabrice Thalmann2 — 1Department of Chemistry King’s College London, London, UK — 2Institut Charles Sadron, Strasbourg, France
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.