Interpretable machine learning for thermoelectric materials design with Kolmogorov-Arnold networks.

The discovery of high-performance thermoelectric materials requires models that are both accurate and interpretable. Traditional machine learning approaches, while effective at property prediction, often act as black boxes and provide limited physical insight. In this work, we introduce Kolmogorov--Arnold Networks (KANs) for the prediction of thermoelectric properties, focusing on the Seebeck coefficient and band gap. Compared to multilayer perceptrons (MLPs), KANs achieve comparable predictive accuracy while offering explicit symbolic representations of structure--property relationships. This dual capability enables both reliable predictions and the extraction of physically meaningful functional forms. Benchmarking against literature models further highlights the robustness and generalisability of the approach. Our findings demonstrate that KANs provide a powerful framework for reverse engineering materials with targeted thermoelectric properties, bridging the gap between predictive performance and scientific interpretability.