ReviewiScience2021
Opportunities and challenges of text mining in aterials research.
Review in iScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A review on knowledge and information extraction from PDF documents and storage approaches.Frontiers in artificial intelligence · 2025Pooled it
- Journal research data policies in materials science.Digital discovery · 2026Review
- Sustainable Materials Design With Multi-Modal Artificial Intelligence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Assessing the performance of multimodal large language models in experimental information extraction from liquid-liquid phase separation literature.Scientific reports · 2026Article
- Text Mining of CVD Synthesis Recipes for 2D Materials.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Language Models Enable Data-Augmented Synthesis Planning for Inorganic Materials.ACS applied materials & interfaces · 2025Article
- A global analysis of the rise, reign, and retreat of topics in research toward sustainable platform chemicals.Green chemistry : an international journal and green chemistry resource : GC · 2025Article
- A Holistic Data-Driven Approach to Synthesis Predictions of Colloidal Nanocrystal Shapes.Journal of the American Chemical Society · 2025Article
- Machine Learning-Aided Inverse Design and Discovery of Novel Polymeric Materials for Membrane Separation.Environmental science & technology · 2025Review
- Assessment of fine-tuned large language models for real-world chemistry and material science applications.Chemical science · 2025Article
- A document-level information extraction pipeline for layered cathode materials for sodium-ion batteries.Scientific data · 2024Article
- Extracting accurate materials data from research papers with conversational language models and prompt engineering.Nature communications · 2024Article
- Patterns Lead the Way to Far-from-Equilibrium Materials.ACS physical chemistry Au · 2024Review
- Text Mining the Literature to Inform Experiments and Rationalize Impurity Phase Formation for BiFeOChemistry of materials : a publication of the American Chemical Society · 2024Article
- Language models and protocol standardization guidelines for accelerating synthesis planning in heterogeneous catalysis.Nature communications · 2023Article
- Design principles for NASICON super-ionic conductors.Nature communications · 2023Article
- Data quantity governance for machine learning in materials science.National science review · 2023Article
- Fatigue database of additively manufactured alloys.Scientific data · 2023Article
- BatteryBERT: A Pretrained Language Model for Battery Database Enhancement.Journal of chemical information and modeling · 2022Article
- Computational modeling to assist in the discovery of supramolecular materials.Annals of the New York Academy of Sciences · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Research publications are the major repository of scientific knowledge. However, their unstructured and highly heterogenous format creates a significant obstacle to large-scale analysis of the information contained within. Recent progress in natural language processing (NLP) has provided a variety of tools for high-quality information extraction from unstructured text. These tools are primarily trained on non-technical text and struggle to produce accurate results when applied to scientific text, involving specific technical terminology. During the last years, significant efforts in information retrieval have been made for biomedical and biochemical publications. For materials science, text mining (TM) methodology is still at the dawn of its development. In this review, we survey the recent progress in creating and applying TM and NLP approaches to materials science field. This review is directed at the broad class of researchers aiming to learn the fundamentals of TM as applied to the materials science publications.
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.