In this context, the application of machine learning (ML) has been applied to many fields , including in materials science, where it has shifted the paradigm of data-driven approaches in predicting new materials with desired properties (inverse design) ML models can learn complex nonlinear relationships between the material composition and its properties, allowing for faster and more accurate predictions. Traditional ML models rely heavily on the feature representations of the chemical environment (e.g., nature of bond, composition, etc.), i.e., descriptors , which can limit their performance. However, choosing appropriate descriptors that accurately capture the underlying properties of materials is challenging and can limit the performance of ML models and leads to significant variability in their performance. Another constraint is the scarcity and heterogeneity of data impose additional challenges in training and validating ML models.