The use of machine learning to develop neural network potentials (NNP) representing the interatomic potential energy surface allows us to achieve an optimal balance between accuracy and efficiency in computer simulation of materials. A key point in developing such potentials is the preparation of a training dataset of ab initio trajectories. Here we apply a deep potential molecular dynamics (DeePMD) approach to develop NNP for silica, which is the representative glassformer widely used as a model system for simulating network-forming liquids and glasses. We show that the use of a relatively small training dataset of high-temperature ab initio configurations is enough to fabricate NNP, which describes well both structural and dynamical properties of liquid silica. In particular, we calculate the pair correlation functions, angular distribution function, velocity autocorrelation functions, vibrational density of states, and mean-square displacement and reveal a close agreement with ab initio data. We show that NNP allows us to expand significantly the time-space scales achievable in simulations and thus calculating dynamical and transport properties with more accuracy than that for ab initio methods. We find that developed NNP allows us to describe the structure of the glassy silica with satisfactory accuracy even though no low-temperature configurations were included in the training procedure. The results obtained open up prospects for simulating structural and dynamical properties of liquids and glasses via NNP.

中文翻译：

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液体二氧化硅的深度机器学习原子间势

使用机器学习来开发表示原子间势能面的神经网络电势（NNP），使我们能够在材料的计算机模拟中实现精度和效率之间的最佳平衡。开发这种潜力的关键是准备一个从头算轨迹的训练数据集。在这里，我们采用深层潜在分子动力学（DeePMD）方法开发用于二氧化硅的NNP，这是代表性的玻璃形成剂，被广泛用作模拟网络形成液体和玻璃的模型系统。我们证明了使用相对较小的高温从头算训练数据集构型足以制造NNP，可以很好地描述液态二氧化硅的结构和动力学特性。特别是，我们计算了对相关函数，角度分布函数，速度自相关函数，状态的振动密度和均方位移，并揭示了与从头算数据的紧密一致性。我们表明，NNP使我们能够显着扩展仿真中可以实现的时空尺度，从而比从头算起更精确地计算动力和传输特性方法。我们发现，即使在培训过程中未包括低温配置，开发的NNP仍可以使我们以令人满意的精度描述玻璃状二氧化硅的结构。结果为通过NNP模拟液体和玻璃的结构和动力学性质开辟了前景。