dPOLY: Deep Learning of Polymer Phases and Phase Transition

TL;DR

dPOLY is a deep learning framework that analyzes molecular dynamics data to accurately predict polymer phases and phase transitions, demonstrating broad applicability in soft material research.

Contribution

The paper introduces a novel AI tool combining unsupervised and supervised deep neural networks for phase analysis in polymers from molecular dynamics data.

Findings

Accurately predicts coil to globule transition temperatures.

Effectively maps complex trajectories to lower-dimensional representations.

Demonstrates generality across different polymer sizes.

Abstract

Machine learning (ML) and artificial intelligence (AI) have the remarkable ability to classify, recognize, and characterize complex patterns and trends in large data sets. Here, we adopt a subclass of machine learning methods viz., deep learnings and develop a general-purpose AI tool - dPOLY for analyzing molecular dynamics trajectory and predicting phases and phase transitions in polymers. An unsupervised deep neural network is used within this framework to map a molecular dynamics trajectory undergoing thermophysical treatment such as cooling, heating, drying, or compression to a lower dimension. A supervised deep neural network is subsequently developed based on the lower dimensional data to characterize the phases and phase transition. As a proof of concept, we employ this framework to study coil to globule transition of a model polymer system. We conduct coarse-grained molecular dynamics simulations to collect molecular dynamics trajectories of a single polymer chain over a wide range of temperatures and use dPOLY framework to predict polymer phases. The dPOLY framework accurately predicts the critical temperatures for the coil to globule transition for a wide range of polymer sizes. This method is generic and can be extended to capture various other phase transitions and dynamical crossovers in polymers and other soft materials.