论文标题
$α$ - $β$在机上预测的锆的相变
$α$-$β$ phase transition of zirconium predicted by on-the-fly machine-learned force field
论文作者
论文摘要
在有限温度下对固体结构相变的准确预测是一项具有挑战性的任务，因为动力学非常慢，以至于通常无法通过第一原理（FP）方法直接模拟相变。在这里，我们研究了通过机上机器学习的力场在环境压力下ZR的$α$ - $β$相变。这些是在FP分子动力学（MD）模拟过程中自动生成的，而无需人工干预，同时保持了几乎FP的精度。我们的MD模拟成功地重现了相变的一阶移位性质，这表现为体积的突然跳跃和在相变温度下原子的合作位移。通过模拟的X射线粉末衍射进一步确定相变，预测的相变温度与实验合理一致。此外，我们表明，与常规贝叶斯回归中平方基质的通常反转相比，使用奇异的值分解和设计矩阵的伪反转通常可以改善机器学习的力场。
The accurate prediction of solid-solid structural phase transitions at finite temperature is a challenging task, since the dynamics is so slow that direct simulations of the phase transitions by first-principles (FP) methods are typically not possible. Here, we study the $α$-$β$ phase transition of Zr at ambient pressure by means of on-the-fly machine-learned force fields. These are automatically generated during FP molecular dynamics (MD) simulations without the need of human intervention, while retaining almost FP accuracy. Our MD simulations successfully reproduce the first-order displacive nature of the phase transition, which is manifested by an abrupt jump of the volume and a cooperative displacement of atoms at the phase transition temperature. The phase transition is further identified by the simulated x-ray powder diffraction, and the predicted phase transition temperature is in reasonable agreement with experiment. Furthermore, we show that using a singular value decomposition and pseudo inversion of the design matrix generally improves the machine-learned force field compared to the usual inversion of the squared matrix in the regularized Bayesian regression.
