评估通用机器学习原子间势对多相催化的可迁移性：HetCat26基准
Assessing the Transferability of General-Purpose MachineLearning Interatomic Potentials for Heterogeneous Catalysis with HetCat26
- University of Cambridge（剑桥大学）
- University of Wisconsin-Madison（威斯康星大学麦迪逊分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出HetCat26基准，评估十五个通用机器学习原子间势在多相催化中的可迁移性，发现通用基准无法预测催化性能，并识别出训练数据一致性等关键挑战，为下一代模型开发提供指导。
AI中文摘要：
基础机器学习原子间势（MLIPs）有望在化学和材料科学的广泛领域内达到接近密度泛函理论（DFT）的精度。然而，它们在描述与多相催化相关的系统和过程方面的性能仍未得到充分探索。在此，我们引入HetCat26，一个旨在评估预训练MLIPs在催化建模关键方面性能的基准测试集合，这些方面包括表面能量学、金属-金属氧化物相互作用、吸附以及催化反应网络。通过评估十五个基础模型，我们发现现有通用材料基准上的性能仅能弱预测HetCat26上的性能；从这些通用基准无法推断出对多相催化的可迁移性。在各项基准测试中，当前模型能很好地描述表面能量学，并且令人惊讶的是，也能很好地描述反应势垒，而在吸附方面则观察到较大误差，且DFT位点偏好通常无法重现。尽管如此，eSEN-30M-OAM和MACE-MH-1-OMAT这两个模型在所评估的各项性质上均达到了高精度。除模型基准测试外，HetCat26还强调了训练数据一致性的重要性：PBE和PBE+U计算不应在训练集中混合。总体而言，我们识别出限制当前基础MLIPs向多相催化及更广泛地界面化学反应可迁移性的挑战，为下一代基础模型的开发提供指导。
英文摘要：
Foundation machine learning interatomic potentials (MLIPs) promise near-density functional theory (DFT) accuracy across broad areas of chemistry and materials science. However, their performance in describing systems and processes relevant to heterogeneous catalysis remains underexplored. Here, we introduce HetCat26, a collection of benchmark tests designed to assess pre-trained MLIPs across key aspects of catalytic modeling, including surface energetics, metal-metal oxide interactions, adsorption, and catalytic reaction networks. Evaluating fifteen foundation models, we find that performance on existing general materials benchmarks is only weakly predictive of performance on HetCat26; transferability to heterogeneous catalysis cannot be inferred from these general benchmarks. Across the benchmark tests, current models describe surface energetics and, perhaps surprisingly, reaction barriers well, whereas larger errors are observed for adsorption, and DFT site preferences are often not reproduced. Two models, eSEN-30M-OAM and MACE-MH-1-OMAT, nevertheless achieve high accuracy across the properties evaluated. Beyond model benchmarking, HetCat26 highlights the importance of training data consistency: PBE and PBE+U calculations should not be mixed within a training set. Overall, we identify challenges limiting the transferability of current foundation MLIPs to heterogeneous catalysis and, more broadly, to chemical reactions at interfaces, providing guidance for the development of the next generation of foundation models.
