论文标题
多尺度的机器学习铁磁性和液态铁的原子间潜力
Multiscale machine-learning interatomic potentials for ferromagnetic and liquid iron
论文作者
论文摘要
我们开发和比较了铁的四个原子间潜力：一种简单的机器学习的嵌入原子方法（EAM）电位，具有机器学习的两体和三体依赖性术语的电位，具有机器学习的EAM和三体术语的电位，以及带有SOAP描述符的高斯近似值。所有电势均受使用密度功能理论计算的以身体为中心和液体结构的不同数据库的训练。提出的四个电势代表不同的复杂性水平，并跨越了计算成本的三个数量级。使用立方样条插值对前三个电势进行表达和评估，而第四个电势则在没有额外优化的情况下实现了第四个电位。我们根据所需准确性与计算成本之间的平衡进行比较和讨论每个实施，可转移性和适用性的优势。
We develop and compare four interatomic potentials for iron: a simple machine-learned embedded atom method (EAM) potential, a potential with machine-learned two- and three-body-dependent terms, a potential with machine-learned EAM and three-body terms, and a Gaussian approximation potential with the SOAP descriptor. All potentials are trained to the same diverse database of body-centered cubic and liquid structures computed with density functional theory. The four presented potentials represent different levels of complexity and span three orders of magnitude in computational cost. The first three potentials are tabulated and evaluated efficiently using cubic spline interpolations, while the fourth one is implemented without additional optimization. We compare and discuss the advantages of each implementation, transferability and applicability in terms of the balance between required accuracy versus computational cost.
