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arXiv 2509.03401cond-mat.mtrl-sci
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对大原子基础模型在声子特性上的全面评估和基准研究

A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons

University of South Carolina(南卡罗来纳大学);Georgia Institute of Technology(佐治亚理工学院)

Md Zaibul Anam, Ogheneyoma Aghoghovbia, Mohammed Al-Fahdi, Lingyu Kong, Victor Fung, Ming Hu

AI总结:

本文评估了六种大原子基础模型在声子特性预测中的表现,发现EquiformerV2在预测原子力和三阶IFCs方面表现最佳,而微调版本在预测二阶IFCs、LTC等声子性质上优于其他模型。

摘要:

通用机器学习势(uMLPs)的快速发展使得在广泛的化学空间中高效且准确地预测多种材料性质成为可能。尽管它们在建模声子性质方面的能力正在显现,但系统地在化学多样系统中进行基准测试仍有限。我们评估了六个最近的uMLPs(EquiformerV2、MatterSim、MACE和CHGNet)在Open Quantum Materials Database中的2,429种晶体材料上。模型用于计算位移超晶胞中的原子力,推导原子间力常数(IFCs),并预测声子性质,包括晶格热导率(LTC),并与密度泛函理论(DFT)和实验数据进行比较。EquiformerV2预训练模型在预测原子力和三阶IFCs方面表现强劲,而其微调版本在预测二阶IFCs、LTC和其他声子性质方面始终优于其他模型。尽管MACE和CHGNet在力预测准确性上与EquiformerV2相当,但IFCs拟合中的显著差异导致了较差的LTC预测。相反,MatterSim尽管力准确性较低,但实现了中间的IFCs预测,表明误差抵消和力准确性与声子预测之间的复杂关系。此基准测试指导了uMLPs在具有目标热传输性质的材料高通量筛选中的评估和选择。

英文摘要:

The rapid development of universal machine learning potentials (uMLPs) has enabled efficient, accurate predictions of diverse material properties across broad chemical spaces. While their capability for modeling phonon properties is emerging, systematic benchmarking across chemically diverse systems remains limited. We evaluate six recent uMLPs (EquiformerV2, MatterSim, MACE, and CHGNet) on 2,429 crystalline materials from the Open Quantum Materials Database. Models were used to compute atomic forces in displaced supercells, derive interatomic force constants (IFCs), and predict phonon properties including lattice thermal conductivity (LTC), compared with density functional theory (DFT) and experimental data. The EquiformerV2 pretrained model trained on the OMat24 dataset exhibits strong performance in predicting atomic forces and third-order IFC, while its fine-tuned counterpart consistently outperforms other models in predicting second-order IFC, LTC, and other phonon properties. Although MACE and CHGNet demonstrated comparable force prediction accuracy to EquiformerV2, notable discrepancies in IFC fitting led to poor LTC predictions. Conversely, MatterSim, despite lower force accuracy, achieved intermediate IFC predictions, suggesting error cancellation and complex relationships between force accuracy and phonon predictions. This benchmark guides the evaluation and selection of uMLPs for high-throughput screening of materials with targeted thermal transport properties.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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