Identifying polymer states by machine learning

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.

5 pages, 5 figures

References in corpus (5)

• Deep Learning in Neural Networks: An Overview
• Self-Learning Monte Carlo Method
• Accelerate Monte Carlo Simulations with Restricted Boltzmann Machines
• Machine learning for many-body physics: The case of the Anderson impurity model
• Surface effects in the crystallization process of elastic flexible polymers

Cited by in corpus (17)

• A high-bias, low-variance introduction to Machine Learning for physicists
• Discovering Phases, Phase Transitions and Crossovers through Unsupervised Machine Learning: A critical examination
• Estimating physical properties from liquid crystal textures via machine learning and complexity-entropy methods
• Applications of neural networks to the studies of phase transitions of two-dimensional Potts models
• dPOLY: Deep Learning of Polymer Phases and Phase Transition
• A machine-learning solver for modified diffusion equations
• Machine learning based localization and classification with atomic magnetometers
• A Neural Networks study of the phase transitions of Potts model
• Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction
• Phase diagrams of polymer-containing liquid mixtures with a theory-embedded neural network
• Machine learning phases and criticalities without using real data for training
• Supervised and Unsupervised Machine Learning of Structural Phases of Polymers Adsorbed to Nanowires
• Machine learning for structure-property relationships: Scalability and limitations
• Building Data-driven Models with Microstructural Images: Generalization and Interpretability
• Decoupling approximation robustly reconstructs directed dynamical networks
• Confusion-driven machine learning of structural phases of a flexible, magnetic Stockmayer polymer
• Phase classification using neural networks: application to supercooled, polymorphic core-softened mixtures