Molecular dynamics simulations have been used in different scientific fields to investigate a broad range of physical systems. However, the accuracy of calculation is based on the model considered to describe the atomic interactions. In particular, ab initio molecular dynamics (AIMD) has the accuracy of density functional theory (DFT), and thus is limited to small systems and relatively short simulation time. In this scenario, Neural Network Force Fields (NNFF) have an important role, since it provides a way to circumvent these caveats. In this work we investigate NNFF designed at the level of DFT to describe liquid water, focusing on the size and quality of the training data-set considered. We show that structural properties are less dependent on the size of the training data-set compared to dynamical ones (such as the diffusion coefficient), and a good sampling (selecting data reference for training process) can lead to a small sample with good precision.

中文翻译：

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用于训练液态水神经网络力场的量子力学数据集的大小和质量

分子动力学模拟已被用于不同的科学领域，以研究广泛的物理系统。然而，计算的准确性是基于考虑描述原子相互作用的模型。特别是从头算分子动力学（AIMD）具有密度泛函理论（DFT）的准确性，因此仅限于小型系统和相对较短的模拟时间。在这种情况下，神经网络力场 (NNFF) 具有重要作用，因为它提供了一种规避这些警告的方法。在这项工作中，我们研究了在 DFT 级别设计的用于描述液态水的 NNFF，重点关注所考虑的训练数据集的大小和质量。我们表明，与动态属性（例如扩散系数）相比，结构属性对训练数据集大小的依赖性较小，