🤖 AI Summary
Addressing the scarcity of large-scale labeled data and the limited generalization capability of self-supervised learning (SSL) in materials property prediction, this paper proposes a proxy-label-based supervised graph neural network pretraining framework. The method follows a two-stage paradigm: proxy-label supervised pretraining followed by task-adaptive fine-tuning. Key contributions include: (1) the first introduction of class-level categorical information as proxy labels for supervised pretraining in materials science, significantly enhancing downstream multi-task generalization; and (2) a graph-structure-preserving noise augmentation strategy that injects perturbations while maintaining physical plausibility and topological consistency. Evaluated on six diverse materials property prediction tasks, the proposed approach reduces mean absolute error (MAE) by 2.0%–6.67% over state-of-the-art SSL baselines, establishing new performance benchmarks.
📝 Abstract
Accurate prediction of material properties facilitates the discovery of novel materials with tailored functionalities. Deep learning models have recently shown superior accuracy and flexibility in capturing structure-property relationships. However, these models often rely on supervised learning, which requires large, well-annotated datasets an expensive and time-consuming process. Self-supervised learning (SSL) offers a promising alternative by pretraining on large, unlabeled datasets to develop foundation models that can be fine-tuned for material property prediction. In this work, we propose supervised pretraining, where available class information serves as surrogate labels to guide learning, even when downstream tasks involve unrelated material properties. We evaluate this strategy on two state-of-the-art SSL models and introduce a novel framework for supervised pretraining. To further enhance representation learning, we propose a graph-based augmentation technique that injects noise to improve robustness without structurally deforming material graphs. The resulting foundation models are fine-tuned for six challenging material property predictions, achieving significant performance gains over baselines, ranging from 2% to 6.67% improvement in mean absolute error (MAE) and establishing a new benchmark in material property prediction. This study represents the first exploration of supervised pertaining with surrogate labels in material property prediction, advancing methodology and application in the field.