# Bridging AI and Carbon Capture: A Dataset for LLMs in Ionic Liquids and CBE Research

arXiv: https://arxiv.org/abs/2505.06964 · HTML: https://mlsanity.com/paper/2505.06964/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 35 of its 98 references that are in the map.
- First posted on arXiv 11 May 2025.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)

## Builds on (most cited first, 30 of 35)

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- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
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- [Carbon Emissions and Large Neural Network Training](https://mlsanity.com/paper/2104.10350/) (2021)
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- [Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study](https://mlsanity.com/paper/2303.17466/) (2023)
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- [SeeGULL: A Stereotype Benchmark with Broad Geo-Cultural Coverage Leveraging Generative Models](https://mlsanity.com/paper/2305.11840/) (2023)
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## Abstract (arXiv)

Large Language Models (LLMs) have demonstrated exceptional performance in general knowledge and reasoning tasks across various domains. However, their effectiveness in specialized scientific fields like Chemical and Biological Engineering (CBE) remains underexplored. Addressing this gap requires robust evaluation benchmarks that assess both knowledge and reasoning capabilities in these niche areas, which are currently lacking. To bridge this divide, we present a comprehensive empirical analysis of LLM reasoning capabilities in CBE, with a focus on Ionic Liquids (ILs) for carbon sequestration - an emerging solution for mitigating global warming. We develop and release an expert - curated dataset of 5,920 examples designed to benchmark LLMs' reasoning in this domain. The dataset incorporates varying levels of difficulty, balancing linguistic complexity and domain-specific knowledge. Using this dataset, we evaluate three open-source LLMs with fewer than 10 billion parameters. Our findings reveal that while smaller general-purpose LLMs exhibit basic knowledge of ILs, they lack the specialized reasoning skills necessary for advanced applications. Building on these results, we discuss strategies to enhance the utility of LLMs for carbon capture research, particularly using ILs. Given the significant carbon footprint of LLMs, aligning their development with IL research presents a unique opportunity to foster mutual progress in both fields and advance global efforts toward achieving carbon neutrality by 2050.