Transfer learning across chemical spaces is widely used in materials graph neural networks, yet its success declines sharply as source and target domains diverge. This work frames that limitation in information-theoretic terms by modeling chemical spaces as probability distributions over composition, structure, and properties. We identify mutual information between source and target as the key determinant of transferability and derive a conceptual lower bound on target error governed by source error, shared information, and irreducible noise. The analysis explains why transfer succeeds under strong elemental and structural overlap but fails when chemical spaces are weakly aligned. It also yields a practical criterion: assess domain overlap before fine-tuning. By establishing transferability as an information-constrained problem, the study provides a principled basis for deciding when adaptation is justified in materials GNNs.
Institute for Advanced Materials Research Press