# Identifying polymer states by machine learning

## TL;DR

This paper demonstrates that a simple neural network can effectively classify various polymer configurations and identify phase transition points, offering a novel approach to studying polymer phase behavior.

## Contribution

It shows that neural networks can recognize multiple polymer states and transition points, providing an unconventional tool for phase transition analysis in polymers.

## Key findings

- Neural network accurately classifies different polymer states.
- Network identifies transition points consistent with specific-heat calculations.
- Simple model suffices for complex phase recognition.

## Abstract

The ability of a feed-forward neural network to learn and classify different states of polymer configurations is systematically explored. Performing numerical experiments, we find that a simple network model can, after adequate training, recognize multiple structures, including gas-like coil, liquid-like globular, and crystalline anti-Mackay and Mackay structures. The network can be trained to identify the transition points between various states, which compare well with those identified by independent specific-heat calculations. Our study demonstrates that neural network provides an unconventional tool to study the phase transitions in polymeric systems.