DPmoire: A tool for constructing accurate machine learning force fields in moir\'e systems

Understand

In moir\'e systems, the impact of lattice relaxation on electronic band structures is significant, yet the computational demands of first-principles relaxation are prohibitively high due to the large number of atoms involved.

• To address this challenge, We introduce a robust methodology for the construction of machine learning potentials specifically tailored for moir\'e structures and present an open-source software package DPmoire designed to facilitate this process.
• Utilizing this package, we have developed machine learning force fields (MLFFs) for MX$_2$ (M = Mo, W; X = S, Se, Te) materials.
• Our approach not only streamlines the computational process but also ensures accurate replication of the detailed electronic and structural properties typically observed in density functional theory (DFT) relaxations.