Physics-informed AI framework for multiscale design, discovery, and optimization of polymer nanocomposite...

Area of Science:

• Materials Science
• Computational Materials Science
• Polymer Science

Background:

• Polymer nanocomposites exhibit complex nonlinear viscoelasticity due to multiscale filler-matrix interactions.
• Existing data-driven models (ANN, RNN, GRU, LightGBM) struggle with spatial-temporal dynamics, interfacial energetics, and generalization for microstructure optimization.

Purpose of the Study:

• Develop a Hierarchical Neural Operator-Based Multiscale Learning Framework for accurate prediction of nonlinear viscoelastic responses.
• Enable simultaneous identification and optimization of polymer nanocomposite microstructures.
• Establish a physics-aware data-driven pipeline for materials discovery.

Main Methods:

• Integrated Fourier Neural Operator (FNO) for continuous mechanical response operator learning.
• Employed Convolutional Variational Autoencoder (CVAE) for generative microstructure latent representation.
• Utilized Physics-Guided Multi-Stage Atom Search Optimization (PG-MS-ASO) for constrained microstructure design.

Main Results:

• Achieved high predictive accuracy (RMSE=0.15, MAE=0.10, R²=0.98), outperforming conventional ML models.
• Generated optimized microstructures showing significant performance improvements (ΔTg up to 31°C, G' > 1.09×10⁶ Pa).
• Demonstrated robust linking of microstructure design to macroscopic viscoelastic behavior.

Conclusions:

• The proposed framework offers a generalizable and physics-consistent alternative to conventional machine learning for polymer nanocomposites.
• Enables accelerated materials discovery by efficiently predicting properties and optimizing microstructures.
• Provides a robust pathway for designing materials with tailored viscoelastic properties.