Vahe's innovative approach leverages word embeddings to predict chemical compositions likely associated with thermoelectric materials. By retaining the task within the model, he identified a previously unrecognized composition that could pave the way for new research. This method not only enhances predictive capabilities but also highlights gaps in existing literature, potentially guiding future investigations.
