Machine learning potentials of kaolinite based on the potential energy surfaces of GGA and meta-GGA density functional theory
Machine learning potentials of kaolinite based on the potential energy surfaces of GGA and meta-GGA density functional theory
复制标题
基于GGA势能面和元GGA密度泛函理论的高岭石机器学习势
DOI：
10.1016/j.clay.2022.106596
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发表时间：
2022
影响因子：
5.6
通讯作者：
Okumura Masahiko
中科院分区：
文献类型：
--
作者：
Kobayashi Keita;Yamaguchi Akiko;Okumura Masahiko
机器学习分子动力学（MLMD）是一种很有前途的方法，能够以高精度和低计算成本预测材料特性。基于一个数据集创建了两个高岭土机器学习势 (MLP)，该数据集由 Perdew-Burke-Ernzerhof (PBE) 广义梯度近似 (GGA) 和强约束和适当规范 (SCAN) mata-GGA 泛函的第一原理计算结果组成。高岭石的结构和机械性能通过 MLP 的 MLMD 模拟进行了评估。结果与密度泛函理论模拟、经典分子动力学模拟和实验获得的结果进行了比较。带有基于 SCAN 的 MLP 的 MLMD 在准确评估这些高岭石特性方面表现良好。利用MLP评估了高岭石的振动态密度，并将结果与非弹性中子散射实验数据进行了比较。基于SCAN的MLP与实验结果吻合良好。值得注意的是，MLP 成功地再现了低波数的光谱形状，而评估需要长时间的高精度模拟。
Machine learning molecular dynamics (MLMD) is a promising method for predicting material properties with high accuracy and low computational costs. Two kaolinite machine learning potentials (MLPs) were created based on a dataset that consists of the results of the first-principles calculations with the Perdew–Burke–Ernzerhof (PBE) generalized gradient approximation (GGA) and the strongly constrained and appropriately normed (SCAN) mata-GGA functionals. The structural and mechanical properties of kaolinite were evaluated by the MLMD simulations with the MLPs. The results were compared with those obtained by the density functional theory simulations, classical molecular dynamics simulations, and experiments. The MLMD with the MLP based on SCAN performed well for an accurate evaluation of these kaolinite properties. The vibrational density of states of kaolinite was evaluated using the MLPs, and the results were compared with inelastic neutron scattering experiment data. The MLP based on SCAN agreed well with the experimental results. Remarkably, the MLP successfully reproduced the spectral shape in the low-wavenumber, where evaluation requires a long-time simulation with high accuracy.