ArticleScientific data2022
Perovskite- and Dye-Sensitized Solar-Cell Device Databases 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 18 papers.
What it found
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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.
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Who cites it
18 citing papers in PubMed.
- Data-Driven Materials Science for Energy-Sustainable Applications.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Article
- Development of High-Efficiency Perovskite Solar Cells and Their Integration with Machine Learning.Nanomaterials (Basel, Switzerland) · 2025Review
- Steering towards safe self-driving laboratories.Nature reviews. Chemistry · 2025Review
- Cost-Efficient Domain-Adaptive Pretraining of Language Models for Optoelectronics Applications.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
- 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
- Automatically Generated Datasets: Present and Potential Self-Cleaning Coating Materials.Scientific data · 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
- DECIMER.ai: an open platform for automated optical chemical structure identification, segmentation and recognition in scientific publications.Nature communications · 2023Article
- OpticalBERT and OpticalTable-SQA: Text- and Table-Based Language Models for the Optical-Materials Domain.Journal of chemical information and modeling · 2023Article
- Perovskite- and Dye-Sensitized Solar-Cell Device Databases Auto-generated Using ChemDataExtractor.Scientific data · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The number of scientific publications reporting cutting-edge third-generation photovoltaic devices is increasing rapidly, owing to the pressing need to develop renewable-energy technologies that address the climate-change crisis. Consequently, the field could benefit from a central repository where photovoltaic-performance metrics, such as the power-conversion efficiency (η) are recorded. We present two automatically generated databases that contain photovoltaic properties and device material data for dye-sensitized solar cells (DSCs) and perovskite solar cells (PSCs), totalling 660,881 data entries representing 57,678 photovoltaic devices. The databases were generated by applying the text-mining toolkit ChemDataExtractor on a corpus of 25,720 articles. A multi-faceted evaluation, incorporating manual and automatic methods, was applied to ensure that the data contained therein were of the highest quality, with precision metrics ranging from 73.1% to 95.8%. The DSC database contains 475,045 entries representing 41,680 devices, and the PSC database contains 185,836 entries representing 15,818 devices. The databases are available in MongoDB and JSON formats, which can be queried in Python, R, Java and MATLAB for data-driven photovoltaic materials discovery.
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.