ReviewNano-micro letters2023
Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction.
Review in Nano-micro letters, 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.
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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.
- 900°C thermal-force coupling carbonization tailoring hard carbon microstructures enables high performance sodium-ion pouch batteries.Nature communications · 2026Article
- Machine Learning-Driven Nanoscale Synthesis for Electrocatalytic Performance: From Data-Driven Methodologies to Closed-Loop Optimization.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Synergistic mechanisms of metal-based supports in Ru-based HER catalysts: from dimensional perspective to metal-support interaction engineering.Chemical science · 2026Review
- Electrochemical Corrosion and Safety Hazards in Sustainable Batteries: Corrosion Mechanisms, Safety Challenges, and Protection.Nano-micro letters · 2026Review
- Digital materials ecosystem: from databases to AI agents for autonomous discovery.Chemical science · 2026Review
- Machine learning-guided design of energy-related catalysts from nanoparticles to single-atom sites.Communications chemistry · 2026Review
- Textured and Hierarchically Porous Hematite Photoanode for Efficient Hydrogen Production via Photoelectrochemical Hydrazine Oxidation.Nano-micro letters · 2026Article
- Machine learning approaches for electrocatalyst design in water splitting: a review for green hydrogen production.Frontiers in chemistry · 2026Review
- Critical Progress of Mn, Cu, Co, and V-MOFs and Their Derivatives as Promising Electrodes for Aqueous Zn-Ion Batteries.Nanomaterials (Basel, Switzerland) · 2025Review
- Bi-Interfacial Electron Modulation in CoGels (Basel, Switzerland) · 2025Article
- Hydrogen Evolution Reaction of Electrodeposited Ni-W Films in Acidic Medium and Performance Optimization Using Machine Learning.ChemSusChem · 2025Article
- Material Engineering Strategies for Efficient Hydrogen Evolution Reaction Catalysts.Small methods · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water. Nevertheless, the conventional "trial and error" method for producing advanced electrocatalysts is not only cost-ineffective but also time-consuming and labor-intensive. Fortunately, the advancement of machine learning brings new opportunities for electrocatalysts discovery and design. By analyzing experimental and theoretical data, machine learning can effectively predict their hydrogen evolution reaction (HER) performance. This review summarizes recent developments in machine learning for low-dimensional electrocatalysts, including zero-dimension nanoparticles and nanoclusters, one-dimensional nanotubes and nanowires, two-dimensional nanosheets, as well as other electrocatalysts. In particular, the effects of descriptors and algorithms on screening low-dimensional electrocatalysts and investigating their HER performance are highlighted. Finally, the future directions and perspectives for machine learning in electrocatalysis are discussed, emphasizing the potential for machine learning to accelerate electrocatalyst discovery, optimize their performance, and provide new insights into electrocatalytic mechanisms. Overall, this work offers an in-depth understanding of the current state of machine learning in electrocatalysis and its potential for future research.
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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.