The term compositional generalization is increasingly invoked in machine learning for multi-principal element alloys (MPEAs), yet its meaning remains inconsistent and often conflated with interpolation or extrapolation. In standard machine learning, compositional generalization denotes the structured recombination of known components into novel configurations. Translating this concept to MPEAs is non-trivial due to their continuous composition space, permutation symmetries, and overlapping local atomic environments. Graph neural networks (GNNs), while effective for property prediction, are not inherently designed for such recombination beyond interpolation. This article identifies a resulting boundary problem and proposes an operational definition of compositional generalization based on four criteria: novel element–concentration co-occurrences, extrapolation beyond the convex hull of training compositions, controlled performance degradation, and permutation invariance. The framework clarifies widespread misuses of the term and provides a concrete basis for evaluating and designing models for MPEA discovery.
Institute for Advanced Materials Research Press