ReviewAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2023
Machine Learning Descriptors for Data-Driven Catalysis Study.
Review in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Rethinking catalysis: interpretable AI and description of real-world conditionsFaraday discussions · 2026Article
- Machine learning-guided design of energy-related catalysts from nanoparticles to single-atom sites.Communications chemistry · 2026Review
- Machine learning approaches for electrocatalyst design in water splitting: a review for green hydrogen production.Frontiers in chemistry · 2026Review
- Biomass valorization using 3D-printed catalysts.Communications chemistry · 2025Review
- Accelerating Catalysis Understanding via Large Language Model Data Extraction and Shallow Machine Learning Techniques.JACS Au · 2025Article
- Modeling Time-On-Stream Catalyst Reactivity in the Selective Hydrogenation of Concentrated Acetylene Streams under Industrial Conditions via Experiments and AI.ACS catalysis · 2025Article
- PAH101: A GW+BSE Dataset of 101 Polycyclic Aromatic Hydrocarbon (PAH) Molecular Crystals.Scientific data · 2025Article
- Guided electrocatalyst design through in-situ techniques and data mining approaches.Nano convergence · 2025Review
- Leveraging Machine Learning Potentials for In-Situ Searching of Active sites in Heterogeneous Catalysis.Precision chemistry · 2024Review
- Predicting and understanding photocatalytic COPNAS nexus · 2024Article
- Automation and machine learning augmented by large language models in a catalysis study.Chemical science · 2024Review
- Single-atom and cluster catalysts for thermocatalytic ammonia synthesis at mild conditions.Chemical science · 2024Review
- Machine Learning Descriptors for Data-Driven Catalysis Study.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Traditional trial-and-error experiments and theoretical simulations have difficulty optimizing catalytic processes and developing new, better-performing catalysts. Machine learning (ML) provides a promising approach for accelerating catalysis research due to its powerful learning and predictive abilities. The selection of appropriate input features (descriptors) plays a decisive role in improving the predictive accuracy of ML models and uncovering the key factors that influence catalytic activity and selectivity. This review introduces tactics for the utilization and extraction of catalytic descriptors in ML-assisted experimental and theoretical research. In addition to the effectiveness and advantages of various descriptors, their limitations are also discussed. Highlighted are both 1) newly developed spectral descriptors for catalytic performance prediction and 2) a novel research paradigm combining computational and experimental ML models through suitable intermediate descriptors. Current challenges and future perspectives on the application of descriptors and ML techniques to catalysis are also presented.
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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.