Amorphous ${\mathrm{TiO}}_2$ (a-${\mathrm{TiO}}_2$) is widely used in many fields, ranging from photoelectrochemistry to bioengineering, hence detailed knowledge of its atomic structure is of scientific and technological interest. Here we use an ab initio-based deep neural network potential (DP) to simulate large scale atomic models of crystalline and disordered ${\mathrm{TiO}}_2$ with molecular dynamics. Our DP reproduces the structural properties of all 11 ${\mathrm{TiO}}_2$ crystalline phases, predicts the densities and structure factors of molten and amorphous ${\mathrm{TiO}}_2$ with only a few percent deviation from experiments, and describes the pressure dependence of the amorphous structure in agreement with recent observations. It can be extended to model additional structures and compositions, and can be thus of great value in the study of ${\mathrm{TiO}}_2$-based nanomaterials.

中文翻译：

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基于从头算的深度神经网络模拟的无序TiO2相的结构

非晶态 ${\mathrm{二氧化钛}}_2$ （一种-${\mathrm{二氧化钛}}_2$）广泛应用于从光电化学到生物工程的许多领域，因此对其原子结构的详细了解具有科学技术意义。在这里，我们使用从头算的深度神经网络电位（DP）来模拟晶体和无序的大规模原子模型${\mathrm{二氧化钛}}_2$分子动力学。我们的DP再现了所有11种的结构特性${\mathrm{二氧化钛}}_2$ 结晶相，预测熔融态和非晶态的密度和结构因子 ${\mathrm{二氧化钛}}_2$与实验的偏差仅几个百分点，并与最近的观察结果一致描述了非晶结构的压力依赖性。它可以扩展为对附加的结构和组成进行建模，因此在研究结构方面具有很大的价值。${\mathrm{二氧化钛}}_2$基纳米材料。