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By: Heena T Shaikh and Dr. Kazi Kutubuddin Sayyad Liyakat.
Assistant Professor, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India
Professor, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India
The thermal performance of polymeric and composite systems underpins their suitability for aerospace, electronics, and energy‑storage applications, yet reliable prediction of temperature‑dependent properties remains a formidable challenge. In this work we present a data‑driven framework that harnesses modern machine‑learning (ML) techniques to forecast key thermal metrics – glass‑transition temperature (Tg), melting point (Tm), thermal conductivity (k), and decomposition onset (Td) – from a chemically informed descriptor space. A curated database of ≈ 7 500 entries, spanning homopolymers, block copolymers, fiber‑reinforced composites, and nanofilled hybrids, supplies the training ground for a suite of supervised models (random forests, gradient‑boosted trees, and deep neural networks) and a Bayesian Gaussian‑process surrogate for uncertainty quantification. Feature engineering blends polymer‑chain topology (repeat‑unit fingerprints, degree of polymerization), interfacial chemistry (surface‑energy descriptors, filler‑matrix adhesion parameters), and processing history (cooling rate, cure schedule). Cross‑validation reveals that ensemble‑averaged predictions achieve mean absolute errors of 7°C for Tg, 12°C for Tm, 0.12 W m⁻¹ K⁻¹ for k, and 15°C for Td – substantially surpassing traditional group‑contribution approaches. Crucially, SHAP (SHapley Additive exPlanations) analysis uncovers physically meaningful drivers: pendant‑group polarity dominates Tg, filler aspect ratio governs k, while cure‑temperature hysteresis controls Td. The model’s capacity to interpolate across chemically diverse families enables rapid “what‑if” screening of novel formulations, reducing experimental cycles by an order of magnitude.
Citation:
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