ArticleScientific data2020
A database of battery materials auto-generated using ChemDataExtractor.
Article in Scientific data, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 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
46 citing papers in PubMed.
- Data-Driven Materials Science for Energy-Sustainable Applications.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Article
- Data-driven interfacial regulations through molecular additive screening for batteries and electrocatalysis.Chemical science · 2026Review
- Autogenerating a Domain-Specific Question-Answering Data Set to Enable High-Performing Language Models for Magnetic Materials.Journal of chemical information and modeling · 2026Article
- Leveraging Machine Learning for Accelerated Electrode-Electrolyte Interface Design in Rechargeable Li-Based Batteries.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Reinforcement Learning for Cathode Material Design Through Sequential Decision-Making Frameworks.Nanomaterials (Basel, Switzerland) · 2026Review
- Machine-Learning Predictions of Photoluminescence in Molecules Exhibiting Thermally Activated Delayed Fluorescence with Implicit Experimental Validation.Journal of chemical information and modeling · 2026Article
- Towards a sustainable high-quality materials data ecosystem: frameworks and strategies.National science review · 2026Review
- Digital materials ecosystem: from databases to AI agents for autonomous discovery.Chemical science · 2026Review
- Advancing battery research through large language models: A review.Innovation (Cambridge (Mass.)) · 2026Review
- A dataset of Curie and Néel temperatures auto-generated with ChemDataExtractor and the Snowball algorithm.Scientific data · 2025Article
- AI-Powered Workflow for Constructing Organic Materials Databases from the Literature: Integrating Large Language Models.ACS omega · 2025Article
- Autogenerating a Domain-Specific Question-Answering Data Set from a Thermoelectric Materials Database to Enable High-Performing BERT Models.Journal of chemical information and modeling · 2025Article
- Cost-Efficient Domain-Adaptive Pretraining of Language Models for Optoelectronics Applications.Journal of chemical information and modeling · 2025Article
- MechBERT: Language Models for Extracting Chemical and Property Relationships about Mechanical Stress and Strain.Journal of chemical information and modeling · 2025Article
- Auto-generating a database on the fabrication details of perovskite solar devices.Scientific data · 2025Article
- A Database of Stress-Strain Properties Auto-generated from the Scientific Literature using ChemDataExtractor.Scientific data · 2024Article
- Liquid Phase Exfoliation of 2D Materials and Its Electrochemical Applications in the Data-Driven Future.Precision chemistry · 2024Review
- Classification of battery compounds using structure-free Mendeleev encodings.Journal of cheminformatics · 2024Article
- How Beneficial Is Pretraining on a Narrow Domain-Specific Corpus for Information Extraction about Photocatalytic Water Splitting?Journal of chemical information and modeling · 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
2 authors.
Funding
Abstract
A database of battery materials is presented which comprises a total of 292,313 data records, with 214,617 unique chemical-property data relations between 17,354 unique chemicals and up to five material properties: capacity, voltage, conductivity, Coulombic efficiency and energy. 117,403 data are multivariate on a property where it is the dependent variable in part of a data series. The database was auto-generated by mining text from 229,061 academic papers using the chemistry-aware natural language processing toolkit, ChemDataExtractor version 1.5, which was modified for the specific domain of batteries. The collected data can be used as a representative overview of battery material information that is contained within text of scientific papers. Public availability of these data will also enable battery materials design and prediction via data-science methods. To the best of our knowledge, this is the first auto-generated database of battery materials extracted from a relatively large number of scientific papers. We also provide a Graphical User Interface (GUI) to aid the use of this database.
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.