图神经网络力场可泛化预测晶体缺陷与声子性质，无需训练数据
Generalizability of Graph Neural Network Force Fields for Predicting Solid-State Properties
- 用氩原子的伦纳德-琼斯势训练图神经网络力场
- 零温/有限温下预测完美与非完美晶格的声子谱和空位迁移能垒
- 首次在未见构型上验证了力场对缺陷行为的泛化能力
机器学习力场（MLFF）有望为复杂分子体系提供比从头算模拟更高效的计算替代方案。然而，确保其在训练数据之外的泛化能力对固态材料研究至关重要。本文研究基于图神经网络（GNN）的MLFF在训练数据仅包含伦纳德-琼斯氩原子系统的情况下，对未显式包含的固态现象的描述能力。我们评估了该力场在零温与有限温度下对完美面心立方（FCC）晶体声子密度分布（PDOS）的预测性能，并通过直接分子动力学（MD）和弦方法评估了非完美晶体中空位迁移速率与能量屏障。值得注意的是，空位构型未出现在训练数据中。结果表明，该力场即使在未见构型下仍能良好捕捉关键固态性质，与参考数据高度一致。我们进一步讨论了提升MLFF泛化能力的数据工程策略。提出的基准测试集与评估工作流为可靠应用MLFF研究复杂固态材料奠定了基础。
原文摘要 · Abstract (English)
Machine-learned force fields (MLFFs) promise to offer a computationally efficient alternative to ab initio simulations for complex molecular systems. However, ensuring their generalizability beyond training data is crucial for their wide application in studying solid materials. This work investigates the ability of a graph neural network (GNN)-based MLFF, trained on Lennard-Jones Argon, to describe solid-state phenomena not explicitly included during training. We assess the MLFF's performance in predicting phonon density of states (PDOS) for a perfect face-centered cubic (FCC) crystal structure at both zero and finite temperatures. Additionally, we evaluate vacancy migration rates and energy barriers in an imperfect crystal using direct molecular dynamics (MD) simulations and the string method. Notably, vacancy configurations were absent from the training data. Our results demonstrate the MLFF's capability to capture essential solid-state properties with good agreement to reference data, even for unseen configurations. We further discuss data engineering strategies to enhance the generalizability of MLFFs. The proposed set of benchmark tests and workflow for evaluating MLFF performance in describing perfect and imperfect crystals pave the way for reliable application of MLFFs in studying complex solid-state materials.
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