ArticleNature communications2023
Language models and protocol standardization guidelines for accelerating synthesis planning in heterogeneous catalysis.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
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Who cites it
12 citing papers in PubMed, 53 citations in OpenAlex.
- Fine-tuning large language models to generate single-atom catalyst synthesis procedures.Communications chemistry · 2026Article
- Bayesian Optimization of Catalysis with In-Context Learning.ACS central science · 2026Article
- Prompt Engineering Accelerates the Data-Driven Discovery of Photocatalysts via an LLM-Based Model Ensemble Strategy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Scientific knowledge graph and ontology generation using open large language models.Digital discovery · 2026Article
- Boosting Computational Catalysis and Chemical Reactivity with Artificial Intelligence.Journal of the American Chemical Society · 2026Review
- Machine learning approaches for electrocatalyst design in water splitting: a review for green hydrogen production.Frontiers in chemistry · 2026Review
- Accelerating Catalysis Understanding via Large Language Model Data Extraction and Shallow Machine Learning Techniques.JACS Au · 2025Article
- Synergizing a knowledge graph and large language model for relay catalysis pathway recommendation.National science review · 2025Article
- From Spectra to Structure: AI-PoweredAnalytical chemistry · 2025Article
- Design Principles of Catalytic Materials for COAdvanced materials (Deerfield Beach, Fla.) · 2024Article
- Automation and machine learning augmented by large language models in a catalysis study.Chemical science · 2024Review
- Fine-tuning large language models for chemical text mining.Chemical science · 2024Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Synthesis protocol exploration is paramount in catalyst discovery, yet keeping pace with rapid literature advances is increasingly time intensive. Automated synthesis protocol analysis is attractive for swiftly identifying opportunities and informing predictive models, however such applications in heterogeneous catalysis remain limited. In this proof-of-concept, we introduce a transformer model for this task, exemplified using single-atom heterogeneous catalysts (SACs), a rapidly expanding catalyst family. Our model adeptly converts SAC protocols into action sequences, and we use this output to facilitate statistical inference of their synthesis trends and applications, potentially expediting literature review and analysis. We demonstrate the model's adaptability across distinct heterogeneous catalyst families, underscoring its versatility. Finally, our study highlights a critical issue: the lack of standardization in reporting protocols hampers machine-reading capabilities. Embracing digital advances in catalysis demands a shift in data reporting norms, and to this end, we offer guidelines for writing protocols, significantly improving machine-readability. We release our model as an open-source web application, inviting a fresh approach to accelerate heterogeneous catalysis synthesis planning.
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.