Predicting Polymer Thermal Properties with Machine Learning and Small Language Models

Abstract

Thermal decomposition temperature is a crucial factor in evaluating the thermal stability and practicality of polymer materials. In this study, we explore data-driven approaches for predicting the thermal decomposition temperature of polymers using both classical machine learning (CML) models and a small language model (SLM). We use experimental polymer datasets from the PolyInfo database to train Random Forest and XGBoost models, which utilize molecular fingerprints as structured input features. In contrast, the SLM-based approach directly uses polymer expressed as Simplified Molecular Input Line Entry System (SMILES) strings in textual form, eliminating the need for feature engineering or explicit preprocessing of molecular descriptors. This presents an alternative modeling framework for predicting polymer properties, where structure-property relationships are learned directly from raw chemical representations. In addition to thermal decomposition temperature, we also apply this framework to predict glass transition temperature using a dataset previously reported in our work, demonstrating its potential applicability to multiple polymer thermal properties. Overall, our results suggest that small language models can serve as a valuable alternative modeling strategy for predicting polymer thermal properties, providing a complementary perspective to traditional methods.