论文标题
机器学习的最佳数据生成原子间电位
Optimal data generation for machine learned interatomic potentials
论文作者
论文摘要
机器学习间的原子势（MLIP）通常是原子模拟的，但是用于拟合这些模型的原子配置的数据库是一个费力的过程，需要大量的计算和人为努力。提出了一种计算高效的方法来生成原子配置的数据库，这些数据库包含有关块状晶体物质势能表面的小置换状态的最佳信息。利用非对角性超级电池（NDSC），建议自动过程用于从头算数据。 MLIP适用于Al，W，Mg和Si，它们非常紧密地重现了从头算和弹性特性。该协议可以很容易地适应其他材料，并且可以插入MLIP生成的任何风味的工作流程中。
Machine learning interatomic potentials (MLIPs) are routinely used atomic simulations, but generating databases of atomic configurations used in fitting these models is a laborious process, requiring significant computational and human effort. A computationally efficient method is presented to generate databases of atomic configurations that contain optimal information on the small-displacement regime of the potential energy surface of bulk crystalline matter. Utilising non-diagonal supercell (NDSC), an automatic process is suggested for ab initio data generation. MLIPs were fitted for Al, W, Mg and Si, which very closely reproduce the ab initio phonon and elastic properties. The protocol can be easily adapted to other materials and can be inserted in the workflow of any flavour of MLIP generation.
