AI 中文总结
研究针对晶体性质预测，提出多级图提示学习框架，包含节点级和图级软提示，能捕获潜在特征。该框架轻量级且与现有GNN编码器无缝集成，实验表明显著提升模型性能，还实现跨属性知识转移。
AI 中文摘要
图神经网络已成为快速准确预测各种晶体性质的强大工具。这些模型常将领域特定知识编码到图编码模块中，增加参数大小且性能严重依赖领域专业知识。此外，将所有可能影响特定晶体性质的化学和结构特征明确纳入GNN编码器是一项具有挑战性的任务。在这项工作中，我们提出了一个软提示学习框架，该框架捕获属性预测所需的潜在特征，这些特征未明确提供给GNN。我们引入了一种新颖的多级图提示学习框架，包括节点级和图级软提示。在节点级别，我们捕获不同原子类型的局部化学语义，而在图级别，我们编码晶体图的全局结构对称性。我们提出的提示学习框架轻量级且能与任何现有GNN编码器无缝集成。在流行基准数据集上的广泛实验表明，纳入提示学习显著提高了（3%-15%）最先进GNN模型在晶体性质预测任务中的性能。此外，学习到的软提示实现了跨属性知识转移，提高了训练数据有限的属性的预测性能。代码可在该https URL获取。
英文摘要
Graph Neural Networks have emerged as a powerful tool for the fast and accurate prediction of various crystal properties. These models often encode domain-specific knowledge into their graph encoding modules, which increases their parameter size and makes their performance heavily dependent on domain expertise. Added to this, explicitly incorporating all chemical and structural features, that might influence a specific crystal property into the GNN encoder, is a challenging task. In this work, we propose a soft prompt learning framework that captures latent features essential for property prediction, which are not explicitly provided to the GNN. We introduce a novel multilevel graph prompt learning framework comprising both node-level and graph-level soft prompts. At the node level, we capture the local chemical semantics of different atom types, while at the graph level, we encode the global structural symmetry of the crystal graph. Our proposed prompt learning framework is lightweight and seamlessly integrates with any existing GNN encoder. Extensive experiments on popular benchmark datasets show that incorporating prompt learning significantly improves (3\% - 15\%) the performance of state-of-the-art GNN models in crystal property prediction tasks. Furthermore, the learned soft prompts enable cross-property knowledge transfer, enhancing prediction performance for properties with limited training data. Code is available at https://github.com/shrimonmuke0202/Prompt.git
CommentsAccepted in UAI 2026
