论文标题
从头开始计算的高准确性热力学特性借助机器学习电势
High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials
论文作者
论文摘要
热力学特性的准确预测需要对自由能表面的非常精确的表示。要求是双重的 - 首先，包括相关的有限温度机制，其次是执行计算的密集体积温度网格。用于此类计算的系统工作流程需要计算效率和可靠性，到目前为止尚未在Ab Inlibo中提供。在这里，我们阐明了这样的框架，该框架涉及直接提高采样，热力学整合和机器学习电位，从而使我们能够尤其结合了Anharmonic振动的全部效果。与最先进的方法相比，改进的方法具有五次加速。我们将平衡热力学特性计算到BCC NB，磁FCC NI，FCC AL和HCP MG的熔点，并与实验数据发现了显着的一致性。专门针对NB观察到非谐度的强烈影响。引入的程序为开发从头算热力学数据库铺平了道路。
Accurate prediction of thermodynamic properties requires an extremely accurate representation of the free energy surface. Requirements are twofold -- first, the inclusion of the relevant finite-temperature mechanisms, and second, a dense volume-temperature grid on which the calculations are performed. A systematic workflow for such calculations requires computational efficiency and reliability, and has not been available within an ab initio framework so far. Here, we elucidate such a framework involving direct upsampling, thermodynamic integration and machine-learning potentials, allowing us to incorporate, in particular, the full effect of anharmonic vibrations. The improved methodology has a five-times speed-up compared to state-of-the-art methods. We calculate equilibrium thermodynamic properties up to the melting point for bcc Nb, magnetic fcc Ni, fcc Al and hcp Mg, and find remarkable agreement with experimental data. Strong impact of anharmonicity is observed specifically for Nb. The introduced procedure paves the way for the development of ab initio thermodynamic databases.
