About this Abstract |
Meeting |
MS&T22: Materials Science & Technology
|
Symposium
|
AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
|
Presentation Title |
Deep Learning Approaches for Accelerating Polymer Characterization |
Author(s) |
Tarak Patra |
On-Site Speaker (Planned) |
Tarak Patra |
Abstract Scope |
Developing effective order parameters for accurately classifying structures and phases is vital for advancing the current understanding of polymers. Many of the well-established local order parameters that are used to analyse molecular-scale structures of a material system require a-priori knowledge of the local order and lack transferability. To address this problem, here, we report supervised and unsupervised deep learning approaches that autonomously produce order parameters from molecular dynamics trajectory of a polymer undergoing physical processes such as cooling, compression and drying without any human intervention. Our deep leaning methods have successfully captured multiple phases and their transition from molecular dynamics trajectories and aided in establishing polymer scaling relations. These methods are generic and will enable systematics, accurate and automatic mining of flexible order parameters for characterizing and predicting wide range of phase transformations and dynamical crossovers in polymers and other soft matter. |