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arXiv 2209.04059cond-mat.stat-mechcond-mat.dis-nnphysics.chem-ph
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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

Size and Quality of Quantum Mechanical Data Sets for Training Neural Network Force Fields for Liquid Water

Márcio S. Gomes-Filho, Alberto Torres, Alexandre Reily Rocha, Luana S. Pedroza

摘要:

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所考虑的模型来描述原子相互作用。特别地,从头算分子动力学(AIMD)具有密度泛函理论(DFT)的精度,并且因此限于小系统和相对短的模拟时间。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水平上设计的NNFF来描述液态水,重点关注所考虑的训练数据集的大小和质量。我们表明,与动态数据集(如扩散系数)相比,结构属性对训练数据集的大小依赖性较小,并且良好的采样(选择用于训练过程的数据参考)可以导致具有良好精度的小样本。

英文摘要:

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.

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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