ArticleInnovation (Cambridge (Mass.))2025
Artificial-intelligence-assisted design principle for developing high-performance single-atom catalysts.
Article in Innovation (Cambridge (Mass.)), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Nanozymes for ferroptosis-based cancer theranostics.Bioactive materials · 2026Review
- Strength of Interlayer Metal-Metal Coupling as Key Active Site Configuration and Atomic Descriptor for Single-Atom Catalysts.Journal of the American Chemical Society · 2026Article
- Recent Progress in the Sustainable Synthesis of Imidazole Derivatives: Advances in Multicomponent Reactions, Catalysis, and Green Chemistry.Molecules (Basel, Switzerland) · 2026Review
- Dual-Sabatier Optima: How Reaction Mechanism Determines Activity Volcano Map of Dual-Atom Catalysts for Oxygen Reduction Reaction.Angewandte Chemie (International ed. in English) · 2026Article
- Next-Generation Aerogel Electrocatalysts for Oxygen Reduction Reaction.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Modeling Time-On-Stream Catalyst Reactivity in the Selective Hydrogenation of Concentrated Acetylene Streams under Industrial Conditions via Experiments and AI.ACS catalysis · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI)-assisted approaches are powerful means for advancing catalyst design, as they can significantly accelerate the development of novel catalysts. However, the underlying mechanisms of these approaches often remain elusive, which may lead to unreliable results due to a lack of clear understanding of the involved processes. Herein, we present an AI strategy that combines machine learning (ML) and data mining (DM) to identify high-performance catalysts while elucidating the key factors that govern catalytic performance in complex reactions. Applying this AI strategy to evaluate the electrocatalytic oxygen reduction performance of 10,179 single-atom catalysts (SACs), we identified several high-performance SACs and determined the critical influencers of their activity. Experimental validations further confirm the effectiveness of the AI strategy, with the optimal target Co-S
Indexed as
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.