评测多种通用势函数在沸石结构上的表现，发现机器学习模型更可靠。
Benchmarking Universal Interatomic Potentials on Zeolite Structures
- 对比分析六种通用机器学习势与三种传统力场在沸石中的表现。
- 所有机器学习势均能准确复现实验和密度泛函计算的几何与能量数据。
- eSEN-30M-OAM 模型在各类沸石结构中表现最稳定，适合高通量筛选。
具有广泛元素覆盖和高精度的原子间势函数（IPs）是高效材料发现的强大工具。近年来涌现出多种覆盖周期表大部分元素的通用势函数，但其在特定化学体系中的适用性需谨慎评估。本文以沸石平衡结构为测试基准，比较了两类通用势函数：(i) 通用解析势函数，包括 GFN-FF、UFF、Dreiding；(ii) 预训练通用机器学习势函数（MLIPs），涵盖 CHGNet、ORB-v3、MatterSim、eSEN-30M-OAM、PFP-v7、EquiformerV2-lE4-lF100-S2EFS-OC22。并与已有的定制化势函数 SLC、ClayFF、BSFF 进行对比，以实验数据和带色散校正的密度泛函理论（DFT）结果为参考。测试体系包含纯二氧化硅骨架及含铜、钾和有机阳离子的铝硅酸盐沸石。结果显示，GFN-FF 在通用解析势中表现最佳，但在高度应变的二氧化硅环和铝硅酸盐体系中精度不足。所有机器学习势均可良好复现实验或 DFT 级别的几何结构与能量。其中，eSEN-30M-OAM 模型在所研究的所有沸石结构中表现出最一致的性能。这些结果表明，现代预训练通用机器学习势函数是包含多种组成的沸石高通量筛选流程中切实可行的工具。
原文摘要 · Abstract (English)
Interatomic potentials (IPs) with wide elemental coverage and high accuracy are powerful tools for high-throughput materials discovery. While the past few years witnessed the development of multiple new universal IPs that cover wide ranges of the periodic table, their applicability to target chemical systems should be carefully investigated. We benchmark several universal IPs using equilibrium zeolite structures as testbeds. We select a diverse set of universal IPs encompassing two major categories: (i) universal analytic IPs, including GFN-FF, UFF, and Dreiding; (ii) pretrained universal machine learning IPs (MLIPs), comprising CHGNet, ORB-v3, MatterSim, eSEN-30M-OAM, PFP-v7, and EquiformerV2-lE4-lF100-S2EFS-OC22. We compare them with established tailor-made IPs, SLC, ClayFF, and BSFF using experimental data and density functional theory (DFT) calculations with dispersion correction as the reference. The tested zeolite structures comprise pure silica frameworks and aluminosilicates containing copper species, potassium, and organic cations. We found that GFN-FF is the best among the tested universal analytic IPs, but it does not achieve satisfactory accuracy for highly strained silica rings and aluminosilicate systems. All MLIPs can well reproduce experimental or DFT-level geometries and energetics. Among the universal MLIPs, the eSEN-30M-OAM model shows the most consistent performance across all zeolite structures studied. These findings show that the modern pretrained universal MLIPs are practical tools in zeolite screening workflows involving various compositions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。