Unveiling the Potential of AI for Nanomaterial Morphology Prediction
Abstract
Creation of nanomaterials with specific morphology remains a complex experimental process, even though there is a growing demand for these materials in various industry sectors. This study explores the potential of AI to predict the morphology of nanoparticles within the data availability constraints. For that, we first generated a new multi-modal dataset that is double the size of analogous studies. Then, we systematically evaluated performance of classical machine learning and large language models in prediction of nanomaterial shapes and sizes. Finally, we prototyped a text-to-image system, discussed the obtained empirical results, as well as the limitations and promises of existing approaches.
Cite
Text
Dubrovsky et al. "Unveiling the Potential of AI for Nanomaterial Morphology Prediction." International Conference on Machine Learning, 2024.Markdown
[Dubrovsky et al. "Unveiling the Potential of AI for Nanomaterial Morphology Prediction." International Conference on Machine Learning, 2024.](https://mlanthology.org/icml/2024/dubrovsky2024icml-unveiling/)BibTeX
@inproceedings{dubrovsky2024icml-unveiling,
title = {{Unveiling the Potential of AI for Nanomaterial Morphology Prediction}},
author = {Dubrovsky, Ivan and Dmitrenko, Andrei and Dmitrenko, Aleksei and Serov, Nikita and Vinogradov, Vladimir},
booktitle = {International Conference on Machine Learning},
year = {2024},
pages = {11957-11978},
volume = {235},
url = {https://mlanthology.org/icml/2024/dubrovsky2024icml-unveiling/}
}