用有序材料知识迁移预测高熵钙钛矿的形成能与能隙，效果因性质而异。
Ordered-to-disordered transfer learning with graph neural networks for formation-energy and HOMO-LUMO gap prediction in high-entropy perovskite oxides
- 从有序钙钛矿迁移学习，用GNN模型跨结构预测性能
- 形成能预测迁移效果好，能隙预测受限于局部化学环境
- 引入少量高熵数据可显著提升能隙预测精度
高熵钙钛矿氧化物（HEPOs）是一类化学复杂、功能特性优异的材料，但其庞大的成分空间和化学/结构无序性给性质预测带来巨大挑战。图神经网络（GNN）虽能加速材料空间探索，却常受限于代表性训练数据的缺乏。本文研究了基于GNN的有序到无序迁移学习方法，用于预测HEPOs的形成能和HOMO-LUMO能隙。评估了四种代表性GNN模型（CGCNN、GATGNN、ALIGNN、M3GNet），以理解结构表征在迁移性能中的作用，涵盖成对二体和角三体相互作用。结果表明：形成能预测具有强迁移能力，而能隙预测因对局部化学环境敏感，迁移效果有限。引入少量特定于HEPO的训练数据可显著提升能隙预测性能。通过UMAP的表示层面分析进一步表明，如ALIGNN中编码的三体几何信息对捕捉复杂构效关系、提高迁移能力至关重要。
原文摘要 · Abstract (English)
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.
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