Developing machine learning potential for classical molecular dynamics simulation with superior phonon properties
Wei, Zhiyong 1Zhang, Cheng Kan, Yajing Zhang, Yan Chen, Yunfei
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作者信息
1. Southeast Univ
折叠
Abstract
Classical molecular dynamics is one of the most important methods for exploring material properties and uncovering physical mechanism, but the predicted results strongly depend on the used potentials. Using ab initio molecular dynamics simulations to obtain atomic conformations and the associated energy and force as training set and testing set, we developed a Gaussian approximation potential model for single-layer MoS2 based on the machine learning method. The phonon dispersion relations calculated from the developed potential are compared well to that of density functional theory, which confirms the accuracy of the developed potential in the harmonic interaction. We also calculated the temperature-dependent Raman-active phonon frequency and linewidth of the single-layer MoS2 using classical molecular dynamics. The obtained temperature-dependent phonon frequency and linewidth are compared with the corresponding experimental results, indicating that the developed potential still has high accuracy in the anharmonic interactions. The detailed process and methods to obtain the MoS2 potential in this work can be extended and applied to the development and investigation of the high-precision potential of emerging new materials.
NSTL
Elsevier