Multi-target digital material design via a conditional denoising diffusion probability model
Abstract Multi-target digital material design has been challenging due to the expansive design space and instability of traditional methods in satisfying multiple objectives. This work proposes and demonstrates a customizer based on a classifier-free, conditional denoising diffusion probability mode...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01759-3 |
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| _version_ | 1849332192578109440 |
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| author | Wei Yue Yuan Gao Zhenliang Pan Fanping Sui Liwei Lin |
| author_facet | Wei Yue Yuan Gao Zhenliang Pan Fanping Sui Liwei Lin |
| author_sort | Wei Yue |
| collection | DOAJ |
| description | Abstract Multi-target digital material design has been challenging due to the expansive design space and instability of traditional methods in satisfying multiple objectives. This work proposes and demonstrates a customizer based on a classifier-free, conditional denoising diffusion probability model (cDDPM) to efficiently create the layouts of digital materials meeting the design goal of multiple mechanical properties all together. A case study has been conducted based on a micro mechanical resonator with four pre-assigned resonant frequencies. Using 29,430 samples generated via finite element analysis (FEA), the cDDPM is trained to simultaneously customize up to four vibrational modes, achieving over 95% prediction accuracy. Furthermore, the cDDPM approach also shows superior performances in the single-target customization for up to 99% in prediction accuracy when compared with traditional conditional generative adversarial networks (cGANs). As such, the proposed design framework provides a highly customizable and robust methodology for the design of complicated digital materials. |
| format | Article |
| id | doaj-art-e3bf1311eb4e4fb9bf26d85ac1fee75a |
| institution | Kabale University |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-e3bf1311eb4e4fb9bf26d85ac1fee75a2025-08-20T03:46:17ZengNature Portfolionpj Computational Materials2057-39602025-08-0111111010.1038/s41524-025-01759-3Multi-target digital material design via a conditional denoising diffusion probability modelWei Yue0Yuan Gao1Zhenliang Pan2Fanping Sui3Liwei Lin4Department of Mechanical Engineering, University of CaliforniaDepartment of Mechanical Engineering, University of CaliforniaCollege of Engineering, Purdue UniversityDepartment of Mechanical Engineering, University of CaliforniaDepartment of Mechanical Engineering, University of CaliforniaAbstract Multi-target digital material design has been challenging due to the expansive design space and instability of traditional methods in satisfying multiple objectives. This work proposes and demonstrates a customizer based on a classifier-free, conditional denoising diffusion probability model (cDDPM) to efficiently create the layouts of digital materials meeting the design goal of multiple mechanical properties all together. A case study has been conducted based on a micro mechanical resonator with four pre-assigned resonant frequencies. Using 29,430 samples generated via finite element analysis (FEA), the cDDPM is trained to simultaneously customize up to four vibrational modes, achieving over 95% prediction accuracy. Furthermore, the cDDPM approach also shows superior performances in the single-target customization for up to 99% in prediction accuracy when compared with traditional conditional generative adversarial networks (cGANs). As such, the proposed design framework provides a highly customizable and robust methodology for the design of complicated digital materials.https://doi.org/10.1038/s41524-025-01759-3 |
| spellingShingle | Wei Yue Yuan Gao Zhenliang Pan Fanping Sui Liwei Lin Multi-target digital material design via a conditional denoising diffusion probability model npj Computational Materials |
| title | Multi-target digital material design via a conditional denoising diffusion probability model |
| title_full | Multi-target digital material design via a conditional denoising diffusion probability model |
| title_fullStr | Multi-target digital material design via a conditional denoising diffusion probability model |
| title_full_unstemmed | Multi-target digital material design via a conditional denoising diffusion probability model |
| title_short | Multi-target digital material design via a conditional denoising diffusion probability model |
| title_sort | multi target digital material design via a conditional denoising diffusion probability model |
| url | https://doi.org/10.1038/s41524-025-01759-3 |
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