ArticleNature communications2024
Active learning streamlines development of high performance catalysts for higher alcohol synthesis.
Article in Nature communications, 2024. 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
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
12 citing papers in PubMed.
- Leveraging Large Language Models for Understanding Fundamental Principles of Catalysis.The journal of physical chemistry. C, Nanomaterials and interfaces · 2026Review
- CoAdvanced materials (Deerfield Beach, Fla.) · 2026Article
- Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High-Performance Cu Alloys.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Thermal and Photocatalytic Conversion of COChemSusChem · 2026Review
- Machine learning to predict plasma-based COGreen chemistry : an international journal and green chemistry resource : GC · 2026Article
- Nanomaterial Integration at Liquid-Liquid Interfaces for Green Catalysis.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Emerging Trends and Future Prospects of the Catalytic Conversion of Syngas to Higher Alcohols.ACS omega · 2026Review
- Predictive design of stretchable electrodes with strain-insensitive performance via robotics- and machine learning-integrated workflow.Nature communications · 2026Article
- Machine Learning in Homogeneous Catalysis: Basic Concepts and Best Practices.ACS catalysis · 2026Article
- Integrative catalytic pairs driving complex chemical reactions.Nature reviews. Chemistry · 2025Review
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- Science acceleration and accessibility with self-driving labs.Nature communications · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Developing efficient catalysts for syngas-based higher alcohol synthesis (HAS) remains a formidable research challenge. The chain growth and CO insertion requirements demand multicomponent materials, whose complex reaction dynamics and extensive chemical space defy catalyst design norms. We present an alternative strategy by integrating active learning into experimental workflows, exemplified via the FeCoCuZr catalyst family. Our data-aided framework streamlines navigation of the extensive composition and reaction condition space in 86 experiments, offering >90% reduction in environmental footprint and costs over traditional programs. It identifies the Fe
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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.