19种通用机器学习势函数在断裂能预测上表现差异大，数据质量比模型复杂度更重要。
Surface Stability Modeling with Universal Machine Learning Interatomic Potentials: A Comprehensive Cleavage Energy Benchmarking Study
- 用3.6万组材料表面结构数据，系统测试了19种机器学习势函数的断裂能预测能力。
- 基于非平衡构型训练的数据使平均误差低于6%，稳定表面识别正确率达87%。
- 简单模型+优质数据可媲美复杂模型，计算速度提升10到100倍，适合实际应用。
机器学习原子间势（MLIPs）通过融合量子力学精度与经典模拟效率，推动了计算材料科学的发展，实现了对元素周期表中材料性质的大规模探索。尽管在体相性质预测上成果显著，但尚未有系统评估其对断裂能——决定断裂、催化、表面稳定性和界面现象的关键性质——的预测能力。本文利用此前建立的包含36,718个层状结构的密度泛函理论（DFT）数据库，对19种先进的通用机器学习势（uMLIPs）进行全面基准测试。评估涵盖多种架构、化学组成、晶系、厚度及表面取向。结果表明，训练数据构成远超架构复杂度的影响：基于强调非平衡构型的Open Materials 2024（OMat24）数据集训练的模型，平均绝对百分比误差低于6%，在87%的情况下正确识别热力学最稳定表面终端，且无需显式表面能训练。相比之下，仅用平衡态数据训练的同类模型误差高五倍，而基于表面吸附物数据训练的模型则严重失效，误差上升17倍。值得注意的是，经过合理数据训练的简单架构在精度上可媲美复杂变换器模型，同时实现10至100倍的计算加速。
原文摘要 · Abstract (English)
Machine learning interatomic potentials (MLIPs) have revolutionized computational materials science by bridging the gap between quantum mechanical accuracy and classical simulation efficiency, enabling unprecedented exploration of materials properties across the periodic table. Despite their remarkable success in predicting bulk properties, no systematic evaluation has assessed how well these universal MLIPs (uMLIPs) can predict cleavage energies, a critical property governing fracture, catalysis, surface stability, and interfacial phenomena. Here, we present a comprehensive benchmark of 19 state-of-the-art uMLIPs for cleavage energy prediction using our previously established density functional theory (DFT) database of 36,718 slab structures spanning elemental, binary, and ternary metallic compounds. We evaluate diverse architectural paradigms, analyzing their performance across chemical compositions, crystal systems, thickness, and surface orientations. Our results reveal that training data composition dominates architectural sophistication: models trained on the Open Materials 2024 (OMat24) dataset, which emphasizes non-equilibrium configurations, achieve mean absolute percentage errors below 6% and correctly identify the thermodynamically most stable surface terminations in 87% of cases, without any explicit surface energy training. In contrast, architecturally identical models trained on equilibrium-only datasets show five-fold higher errors, while models trained on surface-adsorbate data fail catastrophically with a 17-fold degradation. Remarkably, simpler architectures trained on appropriate data achieve comparable accuracy to complex transformers while offering 10-100x computational speedup. These findings show that the community should focus on strategic training data generation that captures the relevant physical phenomena.
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