高熵钙钛矿不好算，这论文拿四种图神经网络试了迁移学习，发现形成能能搬，带隙不行，加点目标数据才准。ALIGNN表现最好。
研究用CGCNN、GATGNN、ALIGNN和M3GNet四种图神经网络，将有序钙钛矿的知识迁移到无序高熵钙钛矿氧化物（HEPOs）上。形成能预测能有效迁移，但HOMO-LUMO带隙预测因对局部化学环境敏感而迁移性有限。加入少量HEPO专属训练数据后，带隙预测精度显著提升。UMAP分析表明ALIGNN编码的三体几何信息对捕捉结构-性质关系至关重要。
Ordered-to-disordered transfer learning with graph neural networks for formation-energy and HOMO-LUMO gap prediction in high-entropy perovskite oxides
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.