We introduce CG-Vec, a crystal graph-to-vector framework that replaces iterative message passing with compact, interpretable descriptors coupled to conventional machine learning. Across diverse datasets, CG-Vec matches the accuracy of deep graph networks on large datasets but substantially outperforms them in data-scarce regimes. The advantage is most pronounced for magnetic properties, where existing approaches have struggled: CG-Vec delivers reliable predictions of magnetization in both ferromagnetic and ferrimagnetic systems and of Curie temperature. Beyond magnetism, CG-Vec performs competitively for formation energy and band gap, demonstrating broad applicability. These results establish vectorized representations as a practical and scalable alternative to deep architectures, enabling efficient and interpretable modeling of complex material properties.
Singh et al. (Wed,) studied this question.