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RESEARCH PAPER · ACS Applied Materials & Interfaces · 2022

Using Machine Learning to Predict Oxygen Evolution Activity for Transition Metal Hydroxide Electrocatalysts

Xue Feng Jiang, Yong Wang, Baorui Jia, Xuanhui Qu, Mingli Qin

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Using Machine Learning to Predict Oxygen Evolution Activity for Transition Metal Hydroxide ElectrocatalystsOriginalAnalysis of the oxygen evolution activity of layered double hydroxides (LDHs) using machine learning guidance16Can Artificial Intelligence Boost Developing Electrocatalysts for Efficient Water Splitting to Produce Green Hydrogen?12Transition metal-based self-supported anode for electrocatalytic water splitting at a large current density101Ferrum-doped nickel selenide @tri-nickel diselenide heterostructure electrocatalysts with efficient and stable water splitting for hydrogen and oxygen production53Active learning guides discovery of a champion four-metal perovskite oxide for oxygen evolution electrocatalysis161Comparing the Catalytic Effect of Metals for Energetic Materials: Machine Learning Prediction of Adsorption Energies on Metals10Explainable machine-learning predictions for catalysts in CO 2 -assisted propane oxidative dehydrogenation25Machine Learning Assisted Prediction of the Mass Activity of Platinum-Based Anode Catalysts Supported on a Variety of Carbon Nanostructures for Direct Methanol Fuel Cells0Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation239Understanding the Performance of (Ni−Fe−Co−Ce)Ox‐Based Water Oxidation Catalysts via Explainable Artificial Intelligence Framework21Theoretical Calculation Assisted by Machine Learning Accelerate Optimal Electrocatalyst Finding for Hydrogen Evolution Reaction25Electrochemical energy conversion and storage processes with machine learning18A materials terminology knowledge graph automatically constructed from text corpus21Machine learning accelerated catalysts design for CO reduction: An interpretability and transferability analysis26Forecasting the Mass Activity of Platinum Anode Catalysts on Various Carbon Nanostructures in Direct Methanol Fuel Cells Using Machine Learning1A visual language model enabling intelligent nanomaterial scanning electron micrograph annotation1Machine learning-assisted optimization design for enhanced oxygen evolution reaction based on vanadium-doped nickel–cobalt layered double hydroxides34Machine learning-driven breakthroughs in water electrolysis and supercapacitors17Advanced theoretical modeling methodologies for electrocatalyst design in sustainable energy conversion28Applications of natural language processing and large language models in materials discovery132Parametric Optimization of Transition Metal-Based Nanocomposite Electrocatalysts for Oxygen Evolution Reaction in Alkaline Media4Design and Application of Electrocatalyst Based on Machine Learning33Machine learning-assisted ZnO-doped photocatalytic degradation of rhodamine B: Prediction, analysis, and optimization9A machine learning framework for pseudo-targeted screening of emerging contaminants: A case study of per and poly-fluoroalkyl substances5Machine Learning-Assisted Screening and Performance Prediction of Single-Atom Catalysts for Electrocatalytic CO 2 Conversion to CO11AI-powered language models for alloy design and laser-based manufacturing: A review of NLP applications in materials science16Spatial-adaptive active learning identifies ultra-durable and highly active catalysts for acidic oxygen evolution reaction9Tailoring layered double hydroxide properties via heteroatom doping: engineering electronic structures and nanostructures for superior electrochemical and catalytic performances14Data-driven insight into CO2 electroreduction: Ensemble machine learning for CO Faraday efficiency prediction in single-atom catalysts0Data-driven optimisation of more sustainable high-to-medium entropy Perovskites for oxygen evolution catalysts0Finding the hidden catalytic knowledge from literature data4From prediction to materials design: machine learning in electrocatalytic water splitting1Large language model-enabled machine learning for high performance Nd–Fe–B permanent magnet design1Bridging machine learning and water electrolysis: Concepts, methods, and perspectives3Photo-electrocatalytic water splitting for hydrogen evolution using Ni-based LDH catalysts: Progress and perspectives2Natural light driven high concentration 5-hydroxymethylfurfural oxidation coupled with 4-nitrophenol reduction by machine learning-optimized Cu0.46-Ni3S2@NF1Explainable AI in Electrocatalysis and Photocatalysis: From Catalyst Design to Mechanistic Insights7Interpretable machine learning for descriptor screening of Cu-based CO2 electroreduction toward C2+ products0From Data to Discovery: Crafting Data‐Driven Frameworks for Advancing OER Electrocatalysts0

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  2. Can Artificial Intelligence Boost Developing Electrocatalysts for Efficient Water Splitting to Produce Green Hydrogen?

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  3. Transition metal-based self-supported anode for electrocatalytic water splitting at a large current density

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  4. Ferrum-doped nickel selenide @tri-nickel diselenide heterostructure electrocatalysts with efficient and stable water splitting for hydrogen and oxygen production

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  16. A visual language model enabling intelligent nanomaterial scanning electron micrograph annotation

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  20. Applications of natural language processing and large language models in materials discovery

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  21. Parametric Optimization of Transition Metal-Based Nanocomposite Electrocatalysts for Oxygen Evolution Reaction in Alkaline Media

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