In moirésystems,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 i...In moirésystems,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,Weintroduce a robust methodology for the construction of machine learning potentials specifically tailored for moiréstructures 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.The MLFFs were rigorously validated against standard DFT results,confirming their efficacy in capturing the complex interplay of atomic interactions within these layered materials.展开更多
基金supported by the Science Center of the National Natural Science Foundation of China(Grant no.12188101)the National Key R&D Program of China(Grant no.2023YFA1607400,2024YFA1408400,2022YFA1403800)the National Natural Science Foundation of China(Grant nos.12274436,11925408,11921004).H.W.acknowledges support from the New Cornerstone Science Foundation through the XPLORER PRIZE.The AI-driven experiments,simulations and model training were performed on the robotic AI-Scientist platform of the Chinese Academy of Science.
文摘In moirésystems,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,Weintroduce a robust methodology for the construction of machine learning potentials specifically tailored for moiréstructures 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.The MLFFs were rigorously validated against standard DFT results,confirming their efficacy in capturing the complex interplay of atomic interactions within these layered materials.
