用力信息和对称性破缺提升表面性质预测精度，速度更快更准
FIRE-GNN: Force-informed, Relaxed Equivariance Graph Neural Network for Rapid and Accurate Prediction of Surface Properties
- 融合原子受力信息与表面法向对称性破缺构建图神经网络
- 工作函数预测误差降至0.065 eV，比之前最好模型降低两倍
- 适合材料筛选与表面性质定制化设计，尤其在化学空间广时优势明显
表面功函数和解理能是决定材料在电子发射、半导体器件及异相催化中应用可行性的关键属性。尽管第一性原理计算可准确预测这些性质，但其计算成本高且表面组合空间巨大，使基于密度泛函理论（DFT）的全面筛选不可行。本文提出FIRE-GNN（力感知、松弛等变图神经网络），整合表面法向对称性破缺与机器学习势（MLIP）导出的力信息，在工作函数预测上实现均方绝对误差降低两倍，达到0.065 eV，优于此前最优模型。我们还对比了近期不变与等变架构，分析对称性破缺影响，并评估了分布外泛化能力，结果表明FIRE-GNN在工作函数预测中始终优于其他模型。该模型可高效精准预测广泛化学空间中的工作函数与解理能，助力具有调控表面性质的材料发现。
原文摘要 · Abstract (English)
The work function and cleavage energy of a surface are critical properties that determine the viability of materials in electronic emission applications, semiconductor devices, and heterogeneous catalysis. While first principles calculations are accurate in predicting these properties, their computational expense combined with the vast search space of surfaces make a comprehensive screening approach with density functional theory (DFT) infeasible. Here, we introduce FIRE-GNN (Force-Informed, Relaxed Equivariance Graph Neural Network), which integrates surface-normal symmetry breaking and machine learning interatomic potential (MLIP)-derived force information, achieving a twofold reduction in mean absolute error (down to 0.065 eV) over the previous state-of-the-art for work function prediction. We additionally benchmark recent invariant and equivariant architectures, analyze the impact of symmetry breaking, and evaluate out-of-distribution generalization, demonstrating that FIRE-GNN consistently outperforms competing models for work function predictions. This model enables accurate and rapid predictions of the work function and cleavage energy across a vast chemical space and facilitates the discovery of materials with tuned surface properties
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