Deep generative model for the inverse design of Van der Waals heterostructures
Abstract This study proposes ConditionCDVAE+, a crystal diffusion variational autoencoder (CDVAE) based deep generative model for inverse design of van der Waals (vdW) heterostructures. To address the challenges of traditional experimental methods relying on trial-and-error and existing models strug...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-06432-9 |
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| _version_ | 1849768709019664384 |
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| author | Shikun Gao Qinyuan Huang Chen Huang Cheng Li Kaihao Liu Baisheng Sa Yadong Yu Dezhen Xue Zhe Liu Mengyan Dai |
| author_facet | Shikun Gao Qinyuan Huang Chen Huang Cheng Li Kaihao Liu Baisheng Sa Yadong Yu Dezhen Xue Zhe Liu Mengyan Dai |
| author_sort | Shikun Gao |
| collection | DOAJ |
| description | Abstract This study proposes ConditionCDVAE+, a crystal diffusion variational autoencoder (CDVAE) based deep generative model for inverse design of van der Waals (vdW) heterostructures. To address the challenges of traditional experimental methods relying on trial-and-error and existing models struggling to incorporate target property constraints, this work achieves breakthroughs through three innovative stages: (1) introduce the SE(3)-equivariant graph neural network EquiformerV2 as the encoder-decoder within the CDVAE framework to enhance the generation quality of the model; (2) design a module integrating Low-rank Multimodal Fusion and Generative Adversarial Networks to map properties and structures into a joint latent space; and (3) for the first time propose a generative model for the vdW heterostructures, by conducting experimental validation on the dataset constructed from Janus III–VI vdW heterostructures. Experiments demonstrate that ConditionCDVAE+ achieves optimal root mean square error for crystal reconstruction, with improved generation quality. Density Functional Theory calculations confirms 99.51% of generated samples converge to energy minima, indicating superior ground-state convergence. The effectiveness of the model under conditional guidance has also been extensively validated. This framework provides an efficient solution for target-oriented design of vdW heterostructures and holds promise for accelerating the development of novel optoelectronic devices. |
| format | Article |
| id | doaj-art-15949522db8b43cabed07336d074a3cb |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-15949522db8b43cabed07336d074a3cb2025-08-20T03:03:42ZengNature PortfolioScientific Reports2045-23222025-07-0115111210.1038/s41598-025-06432-9Deep generative model for the inverse design of Van der Waals heterostructuresShikun Gao0Qinyuan Huang1Chen Huang2Cheng Li3Kaihao Liu4Baisheng Sa5Yadong Yu6Dezhen Xue7Zhe Liu8Mengyan Dai9School of Automation and Information Engineering, Sichuan University of Science and EngineeringSchool of Automation and Information Engineering, Sichuan University of Science and EngineeringChemical Defense Institute, Academy of Military SciencesState Key Laboratory for Mechanical Behavior of Materials, Xi’an Jiaotong UniversityChemical Defense Institute, Academy of Military SciencesMultiscale Computational Materials Facility & Materials Genome Institute, School of Materials Science and EngineeringChemical Defense Institute, Academy of Military SciencesState Key Laboratory for Mechanical Behavior of Materials, Xi’an Jiaotong UniversityChemical Defense Institute, Academy of Military SciencesChemical Defense Institute, Academy of Military SciencesAbstract This study proposes ConditionCDVAE+, a crystal diffusion variational autoencoder (CDVAE) based deep generative model for inverse design of van der Waals (vdW) heterostructures. To address the challenges of traditional experimental methods relying on trial-and-error and existing models struggling to incorporate target property constraints, this work achieves breakthroughs through three innovative stages: (1) introduce the SE(3)-equivariant graph neural network EquiformerV2 as the encoder-decoder within the CDVAE framework to enhance the generation quality of the model; (2) design a module integrating Low-rank Multimodal Fusion and Generative Adversarial Networks to map properties and structures into a joint latent space; and (3) for the first time propose a generative model for the vdW heterostructures, by conducting experimental validation on the dataset constructed from Janus III–VI vdW heterostructures. Experiments demonstrate that ConditionCDVAE+ achieves optimal root mean square error for crystal reconstruction, with improved generation quality. Density Functional Theory calculations confirms 99.51% of generated samples converge to energy minima, indicating superior ground-state convergence. The effectiveness of the model under conditional guidance has also been extensively validated. This framework provides an efficient solution for target-oriented design of vdW heterostructures and holds promise for accelerating the development of novel optoelectronic devices.https://doi.org/10.1038/s41598-025-06432-9 |
| spellingShingle | Shikun Gao Qinyuan Huang Chen Huang Cheng Li Kaihao Liu Baisheng Sa Yadong Yu Dezhen Xue Zhe Liu Mengyan Dai Deep generative model for the inverse design of Van der Waals heterostructures Scientific Reports |
| title | Deep generative model for the inverse design of Van der Waals heterostructures |
| title_full | Deep generative model for the inverse design of Van der Waals heterostructures |
| title_fullStr | Deep generative model for the inverse design of Van der Waals heterostructures |
| title_full_unstemmed | Deep generative model for the inverse design of Van der Waals heterostructures |
| title_short | Deep generative model for the inverse design of Van der Waals heterostructures |
| title_sort | deep generative model for the inverse design of van der waals heterostructures |
| url | https://doi.org/10.1038/s41598-025-06432-9 |
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