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.

翻译：机器学习力场（MLFFs）有望为复杂分子体系提供一种计算效率优于从头算模拟的替代方案。然而，确保其在训练数据之外的泛化能力对于其在固态材料研究中的广泛应用至关重要。本研究探讨了一种基于图神经网络（GNN）的MLFF（以Lennard-Jones氩气体系训练）描述训练过程中未显式包含的固态现象的能力。我们评估了该MLFF在零温和有限温度下预测完美面心立方（FCC）晶体结构声子态密度（PDOS）的性能。此外，我们利用直接分子动力学（MD）模拟和弦方法，评估了非完美晶体中空位迁移率及能量势垒。值得注意的是，训练数据中未包含空位构型。我们的结果表明，即使对于未见构型，该MLFF仍能以与参考数据良好的一致性捕捉关键的固态性质。我们进一步讨论了增强MLFF泛化能力的数据工程策略。所提出的基准测试集及评估MLFF描述完美与非完美晶体性能的工作流程，为可靠应用MLFF研究复杂固态材料铺平了道路。