# Unveiling the Potential of AI for Nanomaterial Morphology Prediction

arXiv: https://arxiv.org/abs/2406.02591 · HTML: https://mlsanity.com/paper/2406.02591/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 66 references that are in the map.
- First posted on arXiv 31 May 2024.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Invertible Residual Networks](https://mlsanity.com/paper/1811.00995/) (2018)
- [Revisiting Self-Supervised Visual Representation Learning](https://mlsanity.com/paper/1901.09005/) (2019)
- [A Formal Evaluation of PSNR as Quality Measurement Parameter for Image Segmentation Algorithms](https://mlsanity.com/paper/1605.07116/) (2016)
- [ImageJ2: ImageJ for the next generation of scientific image data](https://mlsanity.com/paper/1701.05940/) (2017)
- [A GPT-4 Reticular Chemist for Guiding MOF Discovery](https://mlsanity.com/paper/2306.14915/) (2023)

## Abstract (arXiv)

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.