ReviewChemical science2024
Automation and machine learning augmented by large language models in a catalysis study.
Review in Chemical science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- Leveraging Large Language Models for Understanding Fundamental Principles of Catalysis.The journal of physical chemistry. C, Nanomaterials and interfaces · 2026Review
- An autonomous lab for data-driven homogeneous catalysis.Nature communications · 2026Article
- Accelerating Catalyst Materials Discovery With Large Artificial Intelligence Models.Angewandte Chemie (International ed. in English) · 2026Review
- MDCrow: automating molecular dynamics workflows with large language models.Machine learning: science and technology · 2026Article
- Boosting Computational Catalysis and Chemical Reactivity with Artificial Intelligence.Journal of the American Chemical Society · 2026Review
- ReactionSeek: LLM-powered literature data mining and knowledge discovery in organic synthesis.Nature communications · 2026Article
- Chemically-informed active learning enables data-efficient multi-objective optimization of self-healing polyurethanes.Chemical science · 2026Article
- AI-Accelerated Discovery of Electrocatalyst Materials.ACS materials Au · 2026Review
- AI and automation: democratizing automation and the evolution towards true AI-autonomous robotics.Chemical science · 2025Review
- Large language model guided automated reaction pathway exploration.Communications chemistry · 2025Article
- Synergizing a knowledge graph and large language model for relay catalysis pathway recommendation.National science review · 2025Article
- Solvent Screening for Separation Processes Using Machine Learning and High-Throughput Technologies.Chem & bio engineering · 2025Review
- Leveraging Prompt Engineering in Large Language Models for Accelerating Chemical Research.ACS central science · 2025Review
- Intelligent Systems for Inorganic Nanomaterial Synthesis.Nanomaterials (Basel, Switzerland) · 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advancements in artificial intelligence and automation are transforming catalyst discovery and design from traditional trial-and-error manual mode into intelligent, high-throughput digital methodologies. This transformation is driven by four key components, including high-throughput information extraction, automated robotic experimentation, real-time feedback for iterative optimization, and interpretable machine learning for generating new knowledge. These innovations have given rise to the development of self-driving labs and significantly accelerated materials research. Over the past two years, the emergence of large language models (LLMs) has added a new dimension to this field, providing unprecedented flexibility in information integration, decision-making, and interacting with human researchers. This review explores how LLMs are reshaping catalyst design, heralding a revolutionary change in the fields.
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.