AI 中文总结
研究针对材料属性预测，提出语义增强几何编码器网络SAGE-Net，通过语义引导消息传递将晶体学语义注入几何消息传递，在多基准测试中表现出色，能有效捕捉晶体学特征，是深度集成多模态材料学习的通用可转移框架。
AI 中文摘要
可靠的结构-属性建模对于加速材料发现至关重要，晶体图和结构衍生的晶体学描述提供了互补的几何和语义信息。现有多模态材料模型主要通过编码后融合、潜在空间对齐或基于注意力的表示交互机制纳入文本信息。然而，大多数情况下，晶体学语义在结构编码后引入，无法直接指导原子级晶体图表示的形成。本文提出语义增强几何编码器网络（SAGE-Net），一个灵活的多模态框架，将描述衍生的化学和晶体学语义注入几何消息传递。SAGE-Net引入语义引导消息传递（SGMP），控制原子级更新，使晶体学语义能直接调节跨多个图神经网络（GNN）主干的局部几何交互。在涵盖带隙、机械、传输相关属性和合成性评估的基准测试中，使用不同GNN主干实例化的SAGE-Net在十个JARVIS-DFT回归目标中的八个上实现了最低平均绝对误差（MAE），并在与基于结构和多模态基线的比较中表现出强大或极具竞争力的性能。对于合成性评估，SAGE-Net展示了出色的分类性能和高召回率。可解释性分析表明，SAGE-Net有效捕捉了可物理解释的晶体学特征，如空间群、维度、多面体环境等。这些结果共同证明了基于SGMP的SAGE-Net是一个用于深度集成多模态材料学习的通用且可转移的框架。
英文摘要
Reliable structure-property modeling is crucial for accelerating materials discovery, where crystal graphs and structure-derived crystallographic descriptions provide complementary geometric and semantic information. Existing multimodal materials models primarily incorporate textual information through post-encoding fusion, latent-space alignment, or attention-based representation interaction mechanisms. However, in most cases, crystallographic semantics are introduced after structural encoding and therefore cannot directly guide the formation of atom-level crystal-graph representations. Here, we present Semantics-Augmented Geometric Encoder Network (SAGE-Net), a flexible multimodal framework that injects description-derived chemical and crystallographic semantics into geometric message passing. SAGE-Net introduces Semantic-Guided Message Passing (SGMP), which gates atom-level updates and enables crystallographic semantics to directly modulate local geometric interactions across multiple graph neural network (GNN) backbones. Across benchmarks covering bandgap, mechanical, transport-related properties, and synthesizability assessment, the SAGE-Net instantiated with different GNN backbones achieves the lowest MAE on eight out of ten JARVIS-DFT regression targets and delivers strong or highly competitive performance against both structure-based and multimodal baselines. For synthesizability assessment, the SAGE-Net demonstrate outstanding classification performance and high recall rates. Interpretability analysis unravels that SAGE-Net effectively captures physically interpretable crystallographic features, viz. space group, dimensionality, polyhedral environments, among others. Together, these results demonstrate SGMP-based SAGE-Net as a general and transferable framework for deeply integrated multimodal materials learning.
Comments29 pages, 5 figures, multi-modal network
