dPOLY: Deep Learning of Polymer Phases and Phase Transition
arXiv:2012.03184 · doi:10.1021/acs.macromol.0c02655
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.
References in corpus (11)
- Machine learning phases of matter
- Learning phase transitions by confusion
- Searching for Activation Functions
- Discovering Phase Transitions with Unsupervised Learning
- Discovering Phases, Phase Transitions and Crossovers through Unsupervised Machine Learning: A critical examination
- Freezing and Collapse of Flexible Polymers on Regular Lattices in Three Dimensions
- Identifying polymer states by machine learning
- Unsupervised machine learning for detection of phase transitions in off-lattice systems I. Foundations
- Unsupervised machine learning for detection of phase transitions in off-lattice systems II. Applications
- Active Learning A Neural Network Model For Gold Clusters \& Bulk From Sparse First Principles Training Data
- A coarse-grained deep neural network model for liquid water