Articlenpj computational materials2023
A rule-free workflow for the automated generation of databases from scientific literature.
Article in npj computational materials, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Artificial Intelligence in the Discovery and Design of Ionic Liquids for Thermal Energy Applications.Journal of chemical information and modeling · 2026Review
- Librarian of Alexandria: A Modular Chemical Data Extraction Pipeline to Compare LLM Performance.Journal of chemical information and modeling · 2026Article
- Towards a sustainable high-quality materials data ecosystem: frameworks and strategies.National science review · 2026Review
- Large Language Models and Machine Learning Framework for Predicting Dental Ceramics Performance.International dental journal · 2026Article
- A review of advances in research on mechanisms inhibiting kidney stone formation from a network pharmacology perspective.Frontiers in pharmacology · 2026Review
- Epidemiologic Method Review at Scale: Assessing Charlson Comorbidity Versioning Using a Large Language Model.medRxiv : the preprint server for health sciences · 2025Article
- The northeast materials database for magnetic materials.Nature communications · 2025Article
- Steering towards safe self-driving laboratories.Nature reviews. Chemistry · 2025Review
- AI driven network pharmacology: Multi-scale mechanisms of traditional Chinese medicine from molecular to patient analysis.Computational and structural biotechnology journal · 2025Review
- Extracting accurate materials data from research papers with conversational language models and prompt engineering.Nature communications · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent times, transformer networks have achieved state-of-the-art performance in a wide range of natural language processing tasks. Here we present a workflow based on the fine-tuning of BERT models for different downstream tasks, which results in the automated extraction of structured information from unstructured natural language in scientific literature. Contrary to existing methods for the automated extraction of structured compound-property relations from similar sources, our workflow does not rely on the definition of intricate grammar rules. Hence, it can be adapted to a new task without requiring extensive implementation efforts and knowledge. We test our data-extraction workflow by automatically generating a database for Curie temperatures and one for band gaps. These are then compared with manually curated datasets and with those obtained with a state-of-the-art rule-based method. Furthermore, in order to showcase the practical utility of the automatically extracted data in a material-design workflow, we employ them to construct machine-learning models to predict Curie temperatures and band gaps. In general, we find that, although more noisy, automatically extracted datasets can grow fast in volume and that such volume partially compensates for the inaccuracy in downstream tasks.
Indexed as
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.