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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.

Generative materials models promise to accelerate discovery by systematically exploring vast regions of chemical space, yet the core concept of “chemical space coverage” remains poorly defined and inconsistently applied across the literature. Researchers routinely claim that their models achieve high “diversity” or “coverage,” but these statements rest on incompatible assumptions about what chemical space actually encompasses. This boundary/definitional article clarifies the term by distinguishing three primary dimensions—compositional space, structural space, and property space—and demonstrates how current diversity metrics conflate or ignore these dimensions, rendering cross-study comparisons unreliable. Drawing on recent advances in generative modelling for crystals and molecules, the analysis shows that a model may report excellent elemental coverage while entirely neglecting novel crystal prototypes or property combinations, or conversely achieve broad structural diversity within a narrow compositional slice. To resolve these ambiguities, the article proposes an operational definition of chemical space coverage built around four explicit, computable metrics: compositional coverage (), structural coverage (), property coverage (), and joint coverage (). Each metric is accompanied by practical boundary conditions that define thresholds for “broad,” “comprehensive,” or “exploratory” coverage. The framework further articulates five essential boundary conditions for sufficiency—task dependence, reference dependence, sparsity adjustment, validity trade-off, and diminishing returns—thereby transforming coverage from a vague aspirational term into a precise evaluative criterion. Adoption of this multi-dimensional framework will enable consistent benchmarking, prevent over-optimistic claims, and guide the responsible development of generative models that truly expand the frontiers of materials design rather than merely resampling known regions. The proposed definitions and boundaries therefore constitute a necessary foundation for the next generation of inverse design methodologies in computational materials engineering.