Combinatorial nature of polymer matrix composites design requires a robust predictive model to accurately predict the

mechanical properties of polymer composites, thereby reducing the need for extensive and costly trial-and-error approaches

in their manufacturing. However, traditional prediction models have been either lacking in accuracy or too resource-intensive

for practical use. This study proposes an advanced Transformer-based predictive model simultaneously considering various

variables that can infl uence mechanical properties, while utilizing only a minimal amount of training data. In developing this

model, we utilize an extensive dataset across 294 types of polymer composites, using a diverse range of polymers and reinforcements,

providing a comprehensive basis for the model’s predictions. The model employs a Transformer-based transfer

learning technique, known for its effi ciency with small datasets, to predict essential mechanical properties such as tensile

strength, tensile modulus, fl exural strength, fl exural modulus and density. It shows high predictive accuracy ( R 2 = 92%) and

makes reliable predictions for combinations of polymer composites that have not been trained on ( R 2 = 82%). Additionally,

the model’s eff ectiveness and learning process are validated through Explainable Artifi cial Intelligence analysis and latent

space visualization.