# Leveraging Large Language Models to Address Data Scarcity in Machine Learning: Applications in Graphene Synthesis

arXiv: https://arxiv.org/abs/2503.04870 · HTML: https://mlsanity.com/paper/2503.04870/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 79 references that are in the map.
- First posted on arXiv 6 Mar 2025.

## Builds on (most cited first, 14 of 14)

- [Fine-Tuned Language Models Generate Stable Inorganic Materials as Text](https://mlsanity.com/paper/2402.04379/) (2024)
- [Extracting Accurate Materials Data from Research Papers with Conversational Language Models and Prompt Engineering](https://mlsanity.com/paper/2303.05352/) (2023)
- [14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon](https://mlsanity.com/paper/2306.06283/) (2023)
- [Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources](https://mlsanity.com/paper/2007.08714/) (2020)
- [word2vec Parameter Learning Explained](https://mlsanity.com/paper/1411.2738/) (2014)
- [ChatGPT Chemistry Assistant for Text Mining and Prediction of MOF Synthesis](https://mlsanity.com/paper/2306.11296/) (2023)
- [GATGPT: A Pre-trained Large Language Model with Graph Attention Network for Spatiotemporal Imputation](https://mlsanity.com/paper/2311.14332/) (2023)
- [Crystal Twins: Self-supervised Learning for Crystalline Material Property Prediction](https://mlsanity.com/paper/2205.01893/) (2022)
- [Data Imputation using Large Language Model to Accelerate Recommendation System](https://mlsanity.com/paper/2407.10078/) (2024)
- [Overcoming data scarcity with transfer learning](https://mlsanity.com/paper/1711.05099/) (2017)
- [On LLM-Enhanced Mixed-Type Data Imputation with High-Order Message Passing](https://mlsanity.com/paper/2501.02191/) (2025)
- [Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery](https://mlsanity.com/paper/2111.01905/) (2021)
- [Machine learning-guided synthesis of advanced inorganic materials](https://mlsanity.com/paper/1905.03938/) (2019)
- [Machine learning driven synthesis of few-layered WTe2](https://mlsanity.com/paper/1910.04603/) (2019)

## Abstract (arXiv)

Machine learning in materials science faces challenges due to limited experimental data, as generating synthesis data is costly and time-consuming, especially with in-house experiments. Mining data from existing literature introduces issues like mixed data quality, inconsistent formats, and variations in reporting experimental parameters, complicating the creation of consistent features for the learning algorithm. Additionally, combining continuous and discrete features can hinder the learning process with limited data. Here, we propose strategies that utilize large language models (LLMs) to enhance machine learning performance on a limited, heterogeneous dataset of graphene chemical vapor deposition synthesis compiled from existing literature. These strategies include prompting modalities for imputing missing data points and leveraging large language model embeddings to encode the complex nomenclature of substrates reported in chemical vapor deposition experiments. The proposed strategies enhance graphene layer classification using a support vector machine (SVM) model, increasing binary classification accuracy from 39% to 65% and ternary accuracy from 52% to 72%. We compare the performance of the SVM and a GPT-4 model, both trained and fine-tuned on the same data. Our results demonstrate that the numerical classifier, when combined with LLM-driven data enhancements, outperforms the standalone LLM predictor, highlighting that in data-scarce scenarios, improving predictive learning with LLM strategies requires more than simple fine-tuning on datasets. Instead, it necessitates sophisticated approaches for data imputation and feature space homogenization to achieve optimal performance. The proposed strategies emphasize data enhancement techniques, offering a broadly applicable framework for improving machine learning performance on scarce, inhomogeneous datasets.