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

• URL: http://arxiv.org/abs/2503.04870v2
• Date: Mon, 10 Mar 2025 14:04:38 GMT
• Title: Leveraging Large Language Models to Address Data Scarcity in Machine Learning: Applications in Graphene Synthesis
• Authors: Devi Dutta Biswajeet, Sara Kadkhodaei,
• Abstract summary: Machine learning in materials science faces challenges due to limited experimental data.<n>We propose strategies that utilize large language models (LLMs) to enhance machine learning performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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