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@@ -13,4 +13,19 @@ This generative model is trained on a large data set (38k) of short single-parti
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  ## Acknowledgements
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- This project is funded by the National Science Foundation Division of Chemical, Bioengineering, Environmental, and Transport Systems under award 2338466, and the American Chemical Society Petroleum Research Fund under award 67239-DNI5.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Acknowledgements
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+ This project is funded by the National Science Foundation Division of Chemical, Bioengineering, Environmental, and Transport Systems under award 2338466, and the American Chemical Society Petroleum Research Fund under award 67239-DNI5.
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+
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+ ## Citation
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+ If you are using this code, please reference our paper:
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+ ```
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+ @article{shabeeb2025learning,
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+ title={Learning the diffusion of nanoparticles in liquid phase TEM via physics-informed generative AI},
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+ author={Shabeeb, Zain and Goyal, Naisargi and Attah Nantogmah, Pagnaa and Jamali, Vida},
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+ journal={Nature Communications},
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+ volume={16},
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+ number={1},
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+ pages={6298},
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+ year={2025},
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+ publisher={Nature Publishing Group UK London}
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+ }
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+ ```