Understanding structural flexibility of metal-organic frameworks (MOFs) via molecular dynamics simulations is crucial to design better MOFs. Density functional theory (DFT) and quantum-chemistry methods provide highly accurate molecular dynamics, but the computational overheads limit their use in long time-dependent simulations. In contrast, classical force fields struggle with the description of coordination bonds. Here we develop a DFT-accurate machine-learning spectral neighbor analysis potentials for two representative MOFs. Their structural and vibrational properties are then studied and tightly compared with available experimental data. Most importantly, we demonstrate an active-learning algorithm, based on mapping the relevant internal coordinates, which drastically reduces the number of training data to be computed at the DFT level. Thus, the workflow presented here appears as an efficient strategy for the study of flexible MOFs with DFT accuracy, but at a fraction of the DFT computational cost.

通过分子动力学模拟了解金属有机框架 （MOF） 的结构柔韧性对于设计更好的 MOF 至关重要。密度泛函理论 （DFT） 和量子化学方法提供了高度精确的分子动力学，但计算开销限制了它们在长时间依赖性模拟中的使用。相比之下，经典力场在描述配位键方面遇到了困难。在这里，我们为两个具有代表性的 MOF 开发了一个 DFT 精确的机器学习光谱邻域分析电位。然后研究它们的结构和振动特性，并与可用的实验数据进行严格比较。最重要的是，我们演示了一种基于映射相关内部坐标的主动学习算法，该算法大大减少了要在 DFT 级别计算的训练数据的数量。因此，这里介绍的工作流程似乎是一种研究柔性 MOF 的有效策略，具有 DFT 精度，但 DFT 计算成本只是其中的一小部分。