SGCR: Structure-Gradient Coupled Replay for Continual Crystal Property Prediction

Crystal structures and property distributions can evolve continuously as new material families and chemical spaces are explored, making continual learning essential for crystal property prediction. We study this problem under limited historical memory and propose \modelname, a structure-gradient coupled framework that jointly considers structural relevance and optimization interference. \modelname first constructs a frozen structural reference space to index a domain-balanced random memory of historical crystals. For each incoming batch, it retrieves historical samples according to both their structural relevance and their conflict with the current prediction objective, enabling more informative use of limited memory. We further introduce a calibrated structural-novelty signal that adaptively modulates gradient correction when current and historical objectives disagree, allowing the model to balance adaptation to newly arriving crystal distributions with retention of previously learned knowledge. Extensive experiments across multiple datasets demonstrate that \modelname consistently improves predictive performance and effectively alleviates catastrophic forgetting.