# Automated, LLM enabled extraction of synthesis details for reticular materials from scientific literature

arXiv: https://arxiv.org/abs/2411.03484 · HTML: https://mlsanity.com/paper/2411.03484/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2024.

## Built on by (most cited first, 7 of 7)

- [Exploring LLMs for Scientific Information Extraction Using The SciEx Framework](https://mlsanity.com/paper/2512.10004/) (2025)
- [Towards Agentic Intelligence for Materials Science](https://mlsanity.com/paper/2602.00169/) (2026)
- [LeMat-Synth: a multi-modal toolbox to curate broad synthesis procedure databases from scientific literature](https://mlsanity.com/paper/2510.26824/) (2025)
- [From Divergence to Consensus: Evaluating the Role of Large Language Models in Facilitating Agreement through Adaptive Strategies](https://mlsanity.com/paper/2503.15521/) (2025)
- [The Prompt Engineering Report Distilled: Quick Start Guide for Life Sciences](https://mlsanity.com/paper/2509.11295/) (2025)
- [Reliable End-to-End Material Information Extraction from the Literature with Source-Tracked Multi-Stage Large Language Models](https://mlsanity.com/paper/2510.05142/) (2025)
- [Extracting ontology-compliant knowledge from scientific text describing irradiated materials using large language models](https://mlsanity.com/paper/2609.17291/) (2026)

## Abstract (arXiv)

Automated knowledge extraction from scientific literature can potentially accelerate materials discovery. We have investigated an approach for extracting synthesis protocols for reticular materials from scientific literature using large language models (LLMs). To that end, we introduce a Knowledge Extraction Pipeline (KEP) that automatizes LLM-assisted paragraph classification and information extraction. By applying prompt engineering with in-context learning (ICL) to a set of open-source LLMs, we demonstrate that LLMs can retrieve chemical information from PDF documents, without the need for fine-tuning or training and at a reduced risk of hallucination. By comparing the performance of five open-source families of LLMs in both paragraph classification and information extraction tasks, we observe excellent model performance even if only few example paragraphs are included in the ICL prompts. The results show the potential of the KEP approach for reducing human annotations and data curation efforts in automated scientific knowledge extraction.