Unveiling the Potential of AI for Nanomaterial Morphology Prediction

Unveiling the Potential of AI for Nanomaterial Morphology Prediction

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 constraint...

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Veröffentlicht in:arXiv.org 2024-05
Hauptverfasser: Dubrovsky, Ivan, Dmitrenko, Andrei, Dmitrenko, Aleksei, Serov, Nikita, Vinogradov, Vladimir
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Artificial intelligence
Large language models
Machine learning
Morphology
Nanomaterials
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creator Dubrovsky, Ivan
Dmitrenko, Andrei
Dmitrenko, Aleksei
Serov, Nikita
Vinogradov, Vladimir
description 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.
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subjects Artificial intelligence
Large language models
Machine learning
Morphology
Nanomaterials
title Unveiling the Potential of AI for Nanomaterial Morphology Prediction
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