The advances of machine-learned force fields have opened up molecular dynamics (MD) simulations for compounds for which ab initio MD is too resource intensive and phenomena for which classical force fields are insufficient. Here we describe a neural-network force field parametrized to reproduce the r2⁢SCAN potential energy landscape of HfO2. Based on an automatic differentiable implementation of the isothermal-isobaric (𝑁⁢𝑃⁢𝑇) ensemble with flexible cell fluctuations, we study the phase space of HfO2. We find excellent predictive capabilities regarding the lattice constants and experimental x-ray diffraction data. The phase transition away from monoclinic is clearly visible at a temperature around 2000 K, in agreement with available experimental data and previous calculations. Another abrupt change in lattice constants occurs around 3000 K. While the resulting lattice constants are closer to cubic, they exhibit a small tetragonal distortion, and there is no associated change in volume. We show that this high-temperature structure is in agreement with the available high-temperature diffraction data.

中文翻译：

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神经网络支持 HfO2 相变的分子动力学研究


机器学习力场的进步为从头 MD 资源密集的化合物和经典力场不足的现象开辟了分子动力学 （MD） 模拟。在这里，我们描述了一个参数化的神经网络力场，以再现 HfO 2 的 r2SCAN 势能景观。基于具有灵活胞波动的等温同压 （NPT） 系综的自动可微分实现，我们研究了 HfO2 的相空间。我们发现了关于晶格常数和实验 X 射线衍射数据的出色预测能力。在 2000 K 左右的温度下，远离单斜晶系的相变清晰可见，这与现有的实验数据和以前的计算一致。晶格常数的另一个突变发生在 3000 K 左右。虽然得到的晶格常数更接近三次方，但它们表现出很小的四边形失真，并且体积没有相关的变化。我们表明，这种高温结构与可用的高温衍射数据一致。