Deep Learning Models for Colloidal Nanocrystal Synthesis
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913611648073728 |
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| author | Gu, Kai Liang, Yingping Su, Jiaming Sun, Peihan Peng, Jia Miao, Naihua Sun, Zhimei Fu, Ying Zhong, Haizheng Zhang, Jun |
| author_facet | Gu, Kai Liang, Yingping Su, Jiaming Sun, Peihan Peng, Jia Miao, Naihua Sun, Zhimei Fu, Ying Zhong, Haizheng Zhang, Jun |
| contents | Colloidal synthesis of nanocrystals usually includes complex chemical reactions and multi-step crystallization processes. Despite the great success in the past 30 years, it remains challenging to clarify the correlations between synthetic parameters of chemical reaction and physical properties of nanocrystals. Here, we developed a deep learning-based nanocrystal synthesis model that correlates synthetic parameters with the final size and shape of target nanocrystals, using a dataset of 3500 recipes covering 348 distinct nanocrystal compositions. The size and shape labels were obtained from transmission electron microscope images using a segmentation model trained with a semi-supervised algorithm on a dataset comprising 1.2 million nanocrystals. By applying the reaction intermediate-based data augmentation method and elaborated descriptors, the synthesis model was able to predict nanocrystal's size with a mean absolute error of 1.39 nm, while reaching an 89% average accuracy for shape classification. The synthesis model shows knowledge transfer capabilities across different nanocrystals with inputs of new recipes. With that, the influence of chemicals on the final size of nanocrystals was further evaluated, revealing the importance order of nanocrystal composition, precursor or ligand, and solvent. Overall, the deep learning-based nanocrystal synthesis model offers a powerful tool to expedite the development of high-quality nanocrystals. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_10838 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Learning Models for Colloidal Nanocrystal Synthesis Gu, Kai Liang, Yingping Su, Jiaming Sun, Peihan Peng, Jia Miao, Naihua Sun, Zhimei Fu, Ying Zhong, Haizheng Zhang, Jun Materials Science Artificial Intelligence Applied Physics Colloidal synthesis of nanocrystals usually includes complex chemical reactions and multi-step crystallization processes. Despite the great success in the past 30 years, it remains challenging to clarify the correlations between synthetic parameters of chemical reaction and physical properties of nanocrystals. Here, we developed a deep learning-based nanocrystal synthesis model that correlates synthetic parameters with the final size and shape of target nanocrystals, using a dataset of 3500 recipes covering 348 distinct nanocrystal compositions. The size and shape labels were obtained from transmission electron microscope images using a segmentation model trained with a semi-supervised algorithm on a dataset comprising 1.2 million nanocrystals. By applying the reaction intermediate-based data augmentation method and elaborated descriptors, the synthesis model was able to predict nanocrystal's size with a mean absolute error of 1.39 nm, while reaching an 89% average accuracy for shape classification. The synthesis model shows knowledge transfer capabilities across different nanocrystals with inputs of new recipes. With that, the influence of chemicals on the final size of nanocrystals was further evaluated, revealing the importance order of nanocrystal composition, precursor or ligand, and solvent. Overall, the deep learning-based nanocrystal synthesis model offers a powerful tool to expedite the development of high-quality nanocrystals. |
| title | Deep Learning Models for Colloidal Nanocrystal Synthesis |
| topic | Materials Science Artificial Intelligence Applied Physics |
| url | https://arxiv.org/abs/2412.10838 |