Unveiling the Potential of AI for Nanomaterial Morphology Prediction

TL;DR

This paper investigates AI's ability to predict nanomaterial morphology, introducing a new dataset and evaluating machine learning models, including large language models, for shape and size prediction, highlighting current capabilities and limitations.

Contribution

The study presents a new, larger multi-modal dataset and systematically compares classical ML and large language models for nanomaterial morphology prediction.

Findings

Large language models show promise but have limitations in accuracy.

Classical machine learning methods perform competitively with LLMs.

The paper discusses the potential and current limitations of AI in nanomaterial design.

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.