AI 中文总结
研究针对异构DFT计算中形成能误差导致多源数据整合困难的问题，采用基于图的迁移学习，利用MatPES数据库训练结构感知图神经网络，将PBE能量转换为r2SCAN级精度，有效升级数据集，推动高性能材料基础模型发展。
AI 中文摘要
异构密度泛函理论（DFT）计算，尤其是平面波实现，会引入系统的形成能误差，误差范围从几十到几百meV/原子，这取决于交换关联泛函、动能截断、赝势和色散校正的选择。MatPES数据集表明，相同结构在PBE和r2SCAN计算之间平均能量差异可达107 meV/原子。这种方法依赖性差异阻碍了多源DFT数据的整合。本文通过基于图的迁移学习解决这一数据孤岛障碍。利用MatPES数据库中380,190个结构配对的PBE - r2SCAN条目，训练结构感知图神经网络预测交叉泛函能量残差并对齐不一致的DFT能量尺度。采用GPTFF模型架构，该模型将传统PBE能量转换为r2SCAN级精度，平均绝对误差为14.3 meV/原子，优于CHGNet的18.2 meV/原子。此方法有效将大量传统PBE数据集升级到高精度r2SCAN标准，能可靠预测相稳定性、电池电压分布和反应热力学，还能整合多源DFT数据推动高性能材料基础模型发展。
英文摘要
Heterogeneous density functional theory (DFT) calculations, particularly plane-wave implementations, introduce systematic formation energy errors ranging from tens to hundreds of meV/atom, depending on the selection of exchange-correlation functionals, kinetic energy cutoffs, pseudopotentials, and dispersion corrections. As demonstrated by the MatPES dataset, identical structures can exhibit an average energy discrepancy of 107 meV/atom between PBE and r2SCAN calculations. Such method-dependent discrepancies hinder the integration of multi-source DFT data, greatly limiting the scale and quality of datasets for training robust materials AI models. Here, we resolve this fundamental data silo barrier via graph-based transfer learning. Leveraging 380,190 structurally paired PBE-r2SCAN entries from the MatPES database, we train a structure-aware graph neural network to predict cross-functional energy residuals and align inconsistent DFT energy scales. By adopting GPTFF model architecture, the model converts conventional PBE energies to r2SCAN-level accuracy with a mean absolute error of 14.3 meV/atom, compared with 18.2 meV/atom achieved by CHGNet. This versatile approach effectively upgrades massive legacy PBE datasets to high-precision r2SCAN standards. It enables reliable predictions of phase stability, battery voltage profiles, and reaction thermodynamics, while allowing the integration of multi-source DFT data to advance the development of high-performance materials foundation models.
