ArticleScientific data2022
Auto-generated database of semiconductor band gaps using ChemDataExtractor.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed.
- Data-Driven Materials Science for Energy-Sustainable Applications.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Article
- Autogenerating a Domain-Specific Question-Answering Data Set to Enable High-Performing Language Models for Magnetic Materials.Journal of chemical information and modeling · 2026Article
- Machine-Learning Predictions of Photoluminescence in Molecules Exhibiting Thermally Activated Delayed Fluorescence with Implicit Experimental Validation.Journal of chemical information and modeling · 2026Article
- High Entropy Alloys Database generated with Large Language Model.Scientific data · 2026Article
- A dataset of Curie and Néel temperatures auto-generated with ChemDataExtractor and the Snowball algorithm.Scientific data · 2025Article
- Steering towards safe self-driving laboratories.Nature reviews. Chemistry · 2025Review
- 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
- MechBERT: Language Models for Extracting Chemical and Property Relationships about Mechanical Stress and Strain.Journal of chemical information and modeling · 2025Article
- A Database of Stress-Strain Properties Auto-generated from the Scientific Literature using ChemDataExtractor.Scientific data · 2024Article
- Creation of a structured solar cell material dataset and performance prediction using large language models.Patterns (New York, N.Y.) · 2024Article
- Extracting accurate materials data from research papers with conversational language models and prompt engineering.Nature communications · 2024Article
- Structured information extraction from scientific text with large language models.Nature communications · 2024Article
- A database of thermally activated delayed fluorescent molecules auto-generated from scientific literature with ChemDataExtractor.Scientific data · 2024Article
- Language models and protocol standardization guidelines for accelerating synthesis planning in heterogeneous catalysis.Nature communications · 2023Article
- Snowball 2.0: Generic Material Data Parser for ChemDataExtractor.Journal of chemical information and modeling · 2023Article
- ReactionDataExtractor 2.0: A Deep Learning Approach for Data Extraction from Chemical Reaction Schemes.Journal of chemical information and modeling · 2023Article
- Article
- OpticalBERT and OpticalTable-SQA: Text- and Table-Based Language Models for the Optical-Materials Domain.Journal of chemical information and modeling · 2023Article
- A rule-free workflow for the automated generation of databases from scientific literature.npj computational materials · 2023Article
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
No grant is acknowledged in the PubMed record.
Abstract
Large-scale databases of band gap information about semiconductors that are curated from the scientific literature have significant usefulness for computational databases and general semiconductor materials research. This work presents an auto-generated database of 100,236 semiconductor band gap records, extracted from 128,776 journal articles with their associated temperature information. The database was produced using ChemDataExtractor version 2.0, a 'chemistry-aware' software toolkit that uses Natural Language Processing (NLP) and machine-learning methods to extract chemical data from scientific documents. The modified Snowball algorithm of ChemDataExtractor has been extended to incorporate nested models, optimized by hyperparameter analysis, and used together with the default NLP parsers to achieve optimal quality of the database. Evaluation of the database shows a weighted precision of 84% and a weighted recall of 65%. To the best of our knowledge, this is the largest open-source non-computational band gap database to date. Database records are available in CSV, JSON, and MongoDB formats, which are machine readable and can assist data mining and semiconductor materials discovery.
Identifiers
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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.