AI 中文总结
该研究针对高熵钙钛矿氧化物的形成能与HOMO-LUMO带隙预测问题，采用图神经网络开展有序到无序迁移学习，发现形成能预测迁移性良好而带隙预测迁移性有限，补充专属数据与三体信息编码可提升性能。
AI 中文摘要
高熵钙钛矿氧化物（HEPOs）是一类化学组成复杂且具有潜在功能特性的材料，但它们庞大的组成空间以及化学、结构无序性对其性能的精准预测构成了重大挑战。图神经网络（GNNs）可实现材料空间的快速探索，但往往受限于代表性训练数据的可用性。本研究利用GNNs开展有序到无序的迁移学习，通过从化学有序的钙钛矿中迁移所学知识，实现对HEPOs的形成能与HOMO-LUMO带隙预测。研究评估了四种具有代表性的GNN模型，包括CGCNN、GATGNN、ALIGNN和M3GNet，以理解结构表示在迁移性能中的作用，涵盖了两两两体相互作用与角三体相互作用。研究发现存在强烈的性能依赖型迁移行为：形成能预测可有效迁移至无序HEPOs，而HOMO-LUMO带隙预测因对局部化学环境敏感，迁移性有限；纳入少量HEPO专属训练数据集可大幅提升HOMO-LUMO带隙预测性能。利用UMAP开展的表示层面分析进一步凸显了编码三体几何信息（如ALIGNN中所做）对捕捉复杂结构-性能关系及提升迁移性的重要性。
英文摘要
High-entropy perovskite oxides (HEPOs) represent a chemically complex class of materials with promising functional properties, yet their vast compositional space and, chemical/structural disorder pose significant challenge for accurate property prediction. Graph neural networks (GNNs) enable rapid exploration of materials space but are often limited by the availability of representative training data. Here, we investigate ordered-to-disordered transfer learning using GNNs for formation-energy and HOMO-LUMO gap prediction in HEPOs by transferring knowledge learned from chemically ordered perovskites. Four representative GNN models, including CGCNN, GATGNN, ALIGNN and M3GNet are evaluated to understand the role of structural representations, spanning pairwise two-body and angular three-body interactions in transfer performance. We find strong property-dependent transfer behavior: formation-energy prediction transfers effectively to disordered HEPOs, whereas HOMO-LUMO gap prediction shows limited transferability due to its sensitivity to local chemical environments. Incorporating a small HEPO-specific training dataset substantially improves HOMO-LUMO gap prediction. Representation-level analysis using UMAP further highlights the importance of encoding three-body geometric information such as in ALIGNN for capturing complex structure-property relationships and improving transferability.
Comments19 pages, 9 figures
