ArticleScientific data2022
A database of refractive indices and dielectric constants auto-generated 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 22 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
22 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
- 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
- High Entropy Alloys Database generated with Large Language Model.Scientific data · 2026Article
- Beating Tunnel Vision: Near-Surface Velocity-Map Imaging.The journal of physical chemistry. A · 2026Article
- A dataset of Curie and Néel temperatures auto-generated with ChemDataExtractor and the Snowball algorithm.Scientific data · 2025Article
- Highly precise optical detection of mass destruction nerve agents based on photonic crystal fibers.Scientific reports · 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
- Dataset of tensorial optical and transport properties of materials from the Wannier function method.Scientific data · 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
- A Database of Stress-Strain Properties Auto-generated from the Scientific Literature using ChemDataExtractor.Scientific data · 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
- Snowball 2.0: Generic Material Data Parser for ChemDataExtractor.Journal of chemical information and modeling · 2023Article
- OpticalBERT and OpticalTable-SQA: Text- and Table-Based Language Models for the Optical-Materials Domain.Journal of chemical information and modeling · 2023Article
- BatteryDataExtractor: battery-aware text-mining software embedded with BERT models.Chemical science · 2022Article
- Optical emissivity dataset of multi-material heterogeneous designs generated with automated figure extraction.Scientific data · 2022Article
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
The ability to auto-generate databases of optical properties holds great potential for advancing optical research, especially with regards to the data-driven discovery of optical materials. An optical property database of refractive indices and dielectric constants is presented, which comprises a total of 49,076 refractive index and 60,804 dielectric constant data records on 11,054 unique chemicals. The database was auto-generated using the state-of-the-art natural language processing software, ChemDataExtractor, using a corpus of 388,461 scientific papers. The data repository offers a representative overview of the information on linear optical properties that resides in scientific papers from the past 30 years. Public availability of these data will enable a quick search for the optical property of certain materials. The large size of this repository will accelerate data-driven research on the design and prediction of optical materials and their properties. To the best of our knowledge, this is the first auto-generated database of optical properties from a large number of scientific papers. We provide a web interface to aid the use of our 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.