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Meeting

2026 TMS Annual Meeting & Exhibition

Symposium

AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling

Presentation Title

Machine Learning Model to Predict Mass Loss during the Catalytic Pyrolysis of Polyetherimide/Graphite Nanocomposites

Author(s)

Masoud Salavati, Stanford White, Mohammed Majdoub, Mine G. Ucak-Astarlioglu, Ahmed Al-Ostaz, Samrat Choudhury, Sasan Nouranian

On-Site Speaker (Planned)

Sasan Nouranian

Abstract Scope

We present a machine learning (ML) framework to predict thermogravimetric behavior and mass loss during the pyrolysis of polyetherimide (PEI)/graphite nanocomposites, catalyzed with Fe, Ni, and Co transition metals. Features such as graphite content, catalyst content, heating rate, temperature, and catalyst properties (i.e., crystal system, d-orbital electrons, lattice parameters, cohesive energy, carbide formation energy, and electrical conductivity), as well as 2D and 3D Avrami-Erofeev kinetic model parameters (i.e., pre-exponential factor and activation energy) were input into the ML models. A transformer-based neural network was trained on formulations containing Fe and Ni and tested on unseen Co-based samples. The model achieved high predictive accuracy (R² = 0.98), even with extrapolated catalyst data, and reproduced experimental TGA curves with strong fidelity. This approach demonstrates the utility of integrating catalytic descriptors and pyrolysis kinetics into ML workflows for predictive modeling of polymer carbonization.

Proceedings Inclusion?

Planned:

Keywords

Polymers, Composites, Machine Learning

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