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

## TL;DR

This study applies machine learning algorithms to classify lipid bilayer phases, providing evidence supporting the two-states model of membrane fluidity through analysis of molecular dynamics data.

## Contribution

The paper introduces a machine learning method to classify lipid phases without assuming the order parameter, supporting the two-states model of membrane phase transitions.

## Key findings

- Machine learning accurately classifies lipid configurations as fluid or gel.
- Results support the two-states model of lipid bilayer phase transitions.
- Method does not rely on prior assumptions about the order parameter.

## 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 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.