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<p>In moiré 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 moiré structures and present an open-source software package <Emphasis Type="BoldItalic">DPmoire</Emphasis> designed to facilitate this process. Utilizing this package, we have developed machine learning force fields (MLFFs) for MX<sub>2</sub> (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. The MLFFs were rigorously validated against standard DFT results, confirming their efficacy in capturing the complex interplay of atomic interactions within these layered materials.</p>

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DPmoire: a tool for constructing accurate machine learning force fields in moiré systems

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月1日

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  • Jiaxuan Liu,
  • Zhong Fang,
  • Hongming Weng,
  • Quansheng Wu

摘要

In moiré 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 moiré 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 MX2 (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. The MLFFs were rigorously validated against standard DFT results, confirming their efficacy in capturing the complex interplay of atomic interactions within these layered materials.