Unveiling the Potential of AI for Nanomaterial Morphology Prediction
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866913453280591872 |
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| author | Dubrovsky, Ivan Dmitrenko, Andrei Dmitrenko, Aleksei Serov, Nikita Vinogradov, Vladimir |
| author_facet | Dubrovsky, Ivan Dmitrenko, Andrei Dmitrenko, Aleksei Serov, Nikita Vinogradov, Vladimir |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_02591 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unveiling the Potential of AI for Nanomaterial Morphology Prediction Dubrovsky, Ivan Dmitrenko, Andrei Dmitrenko, Aleksei Serov, Nikita Vinogradov, Vladimir Machine Learning Artificial Intelligence 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. |
| title | Unveiling the Potential of AI for Nanomaterial Morphology Prediction |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2406.02591 |