DPmoire: A tool for constructing accurate machine learning force fields in moiré systems
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917097490087936 |
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| author | Liu, Jiaxuan Fang, Zhong Weng, Hongming Wu, Quansheng |
| author_facet | Liu, Jiaxuan Fang, Zhong Weng, Hongming Wu, Quansheng |
| contents | 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 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. This development not only enhances our ability to explore the physical properties of moiré systems with reduced computational overhead but also opens new avenues for the study of relaxation effects and their impact on material properties in two-dimensional layered structures. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_19333 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DPmoire: A tool for constructing accurate machine learning force fields in moiré systems Liu, Jiaxuan Fang, Zhong Weng, Hongming Wu, Quansheng Mesoscale and Nanoscale Physics Materials Science 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 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. This development not only enhances our ability to explore the physical properties of moiré systems with reduced computational overhead but also opens new avenues for the study of relaxation effects and their impact on material properties in two-dimensional layered structures. |
| title | DPmoire: A tool for constructing accurate machine learning force fields in moiré systems |
| topic | Mesoscale and Nanoscale Physics Materials Science |
| url | https://arxiv.org/abs/2412.19333 |