A chemical autonomous robotic platform for end-to-end synthesis of nanoparticles
Abstract Traditional nanomaterial development faces inefficiency and unstable results due to labor-intensive trial-and-error methods. To overcome these challenges, we developed a data-driven automated platform integrating artificial intelligence (AI) decision modules with automated experiments. Spec...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-62994-2 |
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| author | Fan Gao Hongqiang Li Zhilong Chen Yunai Yi Shihao Nie Zihao Cheng Zeming Liu Yuanfang Guo Shumin Liu Qizhen Qin Zhengjian Li Lisong Zhang Han Hu Cunjin Li Liang Yang Yunhong Wang Guangxu Chen |
| author_facet | Fan Gao Hongqiang Li Zhilong Chen Yunai Yi Shihao Nie Zihao Cheng Zeming Liu Yuanfang Guo Shumin Liu Qizhen Qin Zhengjian Li Lisong Zhang Han Hu Cunjin Li Liang Yang Yunhong Wang Guangxu Chen |
| author_sort | Fan Gao |
| collection | DOAJ |
| description | Abstract Traditional nanomaterial development faces inefficiency and unstable results due to labor-intensive trial-and-error methods. To overcome these challenges, we developed a data-driven automated platform integrating artificial intelligence (AI) decision modules with automated experiments. Specifically, the platform employs a Generative Pre-trained Transformer (GPT) model to retrieve methods/parameters and implements an A* algorithm centered closed-loop optimization process. It achieves optimized diverse nanomaterials (Au, Ag, Cu2O, PdCu) with controlled types, morphologies, and sizes, demonstrating efficiency and repeatability. Using the A* algorithm, we comprehensively optimized synthesis parameters for multi-target Au nanorods (Au NRs) with longitudinal surface plasmon resonance (LSPR) peak under 600-900 nm across 735 experiments, and for Au nanospheres (Au NSs)/Ag nanocubes (Ag NCs) in 50 experiments. Reproducibility tests showed deviations in characteristic LSPR peak and full width at half maxima (FWHM) of Au NRs under identical parameters were ≤1.1 nm and ≤ 2.9 nm, respectively. Researchers only need initial script editing and parameter input, significantly reducing human resource requirements. Comparative analysis confirms the A* algorithm outperforms Optuna and Olympus in search efficiency, requiring significantly fewer iterations. |
| format | Article |
| id | doaj-art-2e4a0f93aa8e459a9d4eb8201bb3dbba |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-2e4a0f93aa8e459a9d4eb8201bb3dbba2025-08-20T04:02:56ZengNature PortfolioNature Communications2041-17232025-08-0116111310.1038/s41467-025-62994-2A chemical autonomous robotic platform for end-to-end synthesis of nanoparticlesFan Gao0Hongqiang Li1Zhilong Chen2Yunai Yi3Shihao Nie4Zihao Cheng5Zeming Liu6Yuanfang Guo7Shumin Liu8Qizhen Qin9Zhengjian Li10Lisong Zhang11Han Hu12Cunjin Li13Liang Yang14Yunhong Wang15Guangxu Chen16School of Environment and Energy, National Engineering Laboratory for VOCs Pollution Control Technology and Equipment, State Key Laboratory of Luminescent Materials and Devices, Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, South China University of TechnologyZhuhai Fengze Information Technology Co., Ltd.School of Environment and Energy, National Engineering Laboratory for VOCs Pollution Control Technology and Equipment, State Key Laboratory of Luminescent Materials and Devices, Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, South China University of TechnologyZhuhai Fengze Information Technology Co., Ltd.School of Computer Science and Engineering, Beihang UniversitySchool of Computer Science and Engineering, Beihang UniversitySchool of Computer Science and Engineering, Beihang UniversitySchool of Computer Science and Engineering, Beihang UniversitySchool of Environment and Energy, National Engineering Laboratory for VOCs Pollution Control Technology and Equipment, State Key Laboratory of Luminescent Materials and Devices, Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, South China University of TechnologySchool of Environment and Energy, National Engineering Laboratory for VOCs Pollution Control Technology and Equipment, State Key Laboratory of Luminescent Materials and Devices, Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, South China University of TechnologySchool of Environment and Energy, National Engineering Laboratory for VOCs Pollution Control Technology and Equipment, State Key Laboratory of Luminescent Materials and Devices, Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, South China University of TechnologyGuangzhou Ingenious Laboratory Technology Co., Ltd.Guangzhou Ingenious Laboratory Technology Co., Ltd.Guangzhou Ingenious Laboratory Technology Co., Ltd.School of Artificial Intelligence, Hebei University of TechnologySchool of Computer Science and Engineering, Beihang UniversitySchool of Environment and Energy, National Engineering Laboratory for VOCs Pollution Control Technology and Equipment, State Key Laboratory of Luminescent Materials and Devices, Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, South China University of TechnologyAbstract Traditional nanomaterial development faces inefficiency and unstable results due to labor-intensive trial-and-error methods. To overcome these challenges, we developed a data-driven automated platform integrating artificial intelligence (AI) decision modules with automated experiments. Specifically, the platform employs a Generative Pre-trained Transformer (GPT) model to retrieve methods/parameters and implements an A* algorithm centered closed-loop optimization process. It achieves optimized diverse nanomaterials (Au, Ag, Cu2O, PdCu) with controlled types, morphologies, and sizes, demonstrating efficiency and repeatability. Using the A* algorithm, we comprehensively optimized synthesis parameters for multi-target Au nanorods (Au NRs) with longitudinal surface plasmon resonance (LSPR) peak under 600-900 nm across 735 experiments, and for Au nanospheres (Au NSs)/Ag nanocubes (Ag NCs) in 50 experiments. Reproducibility tests showed deviations in characteristic LSPR peak and full width at half maxima (FWHM) of Au NRs under identical parameters were ≤1.1 nm and ≤ 2.9 nm, respectively. Researchers only need initial script editing and parameter input, significantly reducing human resource requirements. Comparative analysis confirms the A* algorithm outperforms Optuna and Olympus in search efficiency, requiring significantly fewer iterations.https://doi.org/10.1038/s41467-025-62994-2 |
| spellingShingle | Fan Gao Hongqiang Li Zhilong Chen Yunai Yi Shihao Nie Zihao Cheng Zeming Liu Yuanfang Guo Shumin Liu Qizhen Qin Zhengjian Li Lisong Zhang Han Hu Cunjin Li Liang Yang Yunhong Wang Guangxu Chen A chemical autonomous robotic platform for end-to-end synthesis of nanoparticles Nature Communications |
| title | A chemical autonomous robotic platform for end-to-end synthesis of nanoparticles |
| title_full | A chemical autonomous robotic platform for end-to-end synthesis of nanoparticles |
| title_fullStr | A chemical autonomous robotic platform for end-to-end synthesis of nanoparticles |
| title_full_unstemmed | A chemical autonomous robotic platform for end-to-end synthesis of nanoparticles |
| title_short | A chemical autonomous robotic platform for end-to-end synthesis of nanoparticles |
| title_sort | chemical autonomous robotic platform for end to end synthesis of nanoparticles |
| url | https://doi.org/10.1038/s41467-025-62994-2 |
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