Hierarchical Sensing Framework for Polymer Degradation Monitoring: A Physics-Constrained Reinforcement Learning...

Area of Science:

• Materials Science
• Artificial Intelligence
• Polymer Chemistry

Background:

• Designing materials with programmable degradation is challenging due to complex structure-property relationships.
• Predicting polymer degradation requires analyzing vast chemical spaces.

Purpose of the Study:

• To develop a novel physics-informed deep learning framework for intelligent characterization and prediction of polymer degradation dynamics.
• To enable real-time monitoring of sustainable materials.

Main Methods:

• A dual-channel sensing architecture fusing Graph Isomorphism Networks and transformer models for multi-scale molecular state detection.
• A physics-constrained policy network ensuring thermodynamic adherence and optimizing degradation pathway exploration.
• Hierarchical signal processing with adaptive weighting and curriculum-based training.

Main Results:

• Experimental validation on 847 novel polymers achieved a 73.2% synthesis success rate.
• Identified 42 structures with precisely monitored degradation profiles (6-24 months).
• Physics-informed constraints improved generated molecular structure validity (94.7%) and diversity (0.82 Tanimoto distance).

Conclusions:

• The framework enhances real-time monitoring for next-generation sustainable materials.
• Physics-informed machine learning advances intelligent sensing in materials science.
• Revealed new correlations between spectroscopic signatures and degradation susceptibility.