ArticleNature communications2024
Structured information extraction from scientific text with large language models.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 144 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
144 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Human researchers are superior to large language models in writing a medical systematic review in a comparative multitask assessment.Scientific reports · 2025Pooled it
- Data-Driven Materials Science for Energy-Sustainable Applications.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Closed-Loop Solid-State Synthesis Planning for Materials Discovery With Large Language Models.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Article
- A text mining and ontology-based approach using phenotypes to obtain relevant literature for rare diseases.iScience · 2026Article
- Autogenerating a Domain-Specific Question-Answering Data Set to Enable High-Performing Language Models for Magnetic Materials.Journal of chemical information and modeling · 2026Article
- A Linear Topology-Aware Alignment Framework for Belt Conveyor Maintenance Knowledge Graph.Sensors (Basel, Switzerland) · 2026Article
- Materials Databases: Foundations of Modern Digital Materials.Precision chemistry · 2026Review
- Agentic genomics: From pipeline automation to autonomous validation.Cell genomics · 2026Review
- AI for Accelerated Materials Discovery: From Generative Design to Autonomous Realization.Chemical reviews · 2026Article
- Large Language Model Guided Discovery of Hole Transport Layer Dopants for Efficient and Stable Perovskite Photovoltaics.Small methods · 2026Article
- Tutorial: guidance on the use of large language models for medical research.Nature protocols · 2026Review
- Challenges and Vision for Standardization of Biopolymer Data Sets for Machine Learning.Biomacromolecules · 2026Review
- TeLLAgent: a dual-agent framework for reliable scientific discovery with tool-enhanced LLMs.Chemical science · 2026Article
- Machine Learning-Empowered Electromagnetic Wave Absorbing Materials: From Forward Prediction to Generative Inverse Design.Molecules (Basel, Switzerland) · 2026Review
- Text-to-flowsheet: an LLM-assisted pipeline for expert-level digitization and automated simulation of chemical processes.Digital discovery · 2026Article
- Article
- Librarian of Alexandria: A Modular Chemical Data Extraction Pipeline to Compare LLM Performance.Journal of chemical information and modeling · 2026Article
- Fine-tuning large language models to generate single-atom catalyst synthesis procedures.Communications chemistry · 2026Article
- Automated Extraction of Multicomponent Alloy Data Using Large Language Models for Sustainable Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
84 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Extracting structured knowledge from scientific text remains a challenging task for machine learning models. Here, we present a simple approach to joint named entity recognition and relation extraction and demonstrate how pretrained large language models (GPT-3, Llama-2) can be fine-tuned to extract useful records of complex scientific knowledge. We test three representative tasks in materials chemistry: linking dopants and host materials, cataloging metal-organic frameworks, and general composition/phase/morphology/application information extraction. Records are extracted from single sentences or entire paragraphs, and the output can be returned as simple English sentences or a more structured format such as a list of JSON objects. This approach represents a simple, accessible, and highly flexible route to obtaining large databases of structured specialized scientific knowledge extracted from research papers.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.