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Updated: May 16, 2026
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Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization
Published on: November 12, 2014
Physics-informed AI framework for multiscale design, discovery, and optimization of polymer nanocomposite
Natrayan Lakshmaiya1, Naveen Kilari2, C H Ajay3
1Department of Research and Innovation, Saveetha School of Engineering, SIMATS, Chennai, 602105, Tamil Nadu, India.
Scientific Reports
|May 14, 2026
Summary
A new Hierarchical Neural Operator framework accurately predicts polymer nanocomposite viscoelasticity and optimizes microstructures. This approach overcomes limitations of traditional machine learning for advanced materials discovery.
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.
