ArticleScientific data2022
Dataset of solution-based inorganic materials synthesis procedures extracted from the scientific literature.
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.
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Who cites it
18 citing papers in PubMed.
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- A multimodal dataset of causal mechanisms in materials science literature.Scientific data · 2026Article
- What are the limits to biomedical research acceleration through general-purpose AI?Scientific reports · 2026Review
- Text-mined dataset of solid-state syntheses with impurity phases using Large Language Model.Scientific data · 2025Article
- Epidemiologic Method Review at Scale: Assessing Charlson Comorbidity Versioning Using a Large Language Model.medRxiv : the preprint server for health sciences · 2025Article
- Steering towards safe self-driving laboratories.Nature reviews. Chemistry · 2025Review
- Dataset exploring the atomic scale structure and ionic dynamics of polyanion sodium cathode materials.Scientific data · 2025Article
- Accurate prediction of synthesizability and precursors of 3D crystal structures via large language models.Nature communications · 2025Article
- Intelligent Systems for Inorganic Nanomaterial Synthesis.Nanomaterials (Basel, Switzerland) · 2025Review
- Auto-generating a database on the fabrication details of perovskite solar devices.Scientific data · 2025Article
- 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
- Text Mining the Literature to Inform Experiments and Rationalize Impurity Phase Formation for BiFeOChemistry of materials : a publication of the American Chemical Society · 2024Article
- Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature.Science advances · 2023Article
- Machine-Learning Rationalization and Prediction of Solid-State Synthesis Conditions.Chemistry of materials : a publication of the American Chemical Society · 2022Article
- Text-mined dataset of gold nanoparticle synthesis procedures, morphologies, and size entities.Scientific data · 2022Article
- Dataset of solution-based inorganic materials synthesis procedures extracted from the scientific literature.Scientific data · 2022Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
The development of a materials synthesis route is usually based on heuristics and experience. A possible new approach would be to apply data-driven approaches to learn the patterns of synthesis from past experience and use them to predict the syntheses of novel materials. However, this route is impeded by the lack of a large-scale database of synthesis formulations. In this work, we applied advanced machine learning and natural language processing techniques to construct a dataset of 35,675 solution-based synthesis procedures extracted from the scientific literature. Each procedure contains essential synthesis information including the precursors and target materials, their quantities, and the synthesis actions and corresponding attributes. Every procedure is also augmented with the reaction formula. Through this work, we are making freely available the first large dataset of solution-based inorganic materials synthesis procedures.
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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.