Issue 34, 2020

A machine learning study of the two states model for lipid bilayer phase transitions

Abstract

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 categorise 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 study provide strong support in favour of a two-states model approach of membrane fluidity.

Graphical abstract: A machine learning study of the two states model for lipid bilayer phase transitions

Supplementary files

Article information

Article type
Paper
Submitted
16 4 2020
Accepted
31 7 2020
First published
12 8 2020

Phys. Chem. Chem. Phys., 2020,22, 19147-19154

A machine learning study of the two states model for lipid bilayer phase transitions

V. Walter, C. Ruscher, O. Benzerara, C. M. Marques and F. Thalmann, Phys. Chem. Chem. Phys., 2020, 22, 19147 DOI: 10.1039/D0CP02058C

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