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RESEARCH PAPER · ACS Omega · 2022

Prediction of Oxygen Evolution Activity for NiCoFe Oxide Catalysts via Machine Learning

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

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Prediction of Oxygen Evolution Activity for NiCoFe Oxide Catalysts via Machine LearningOriginalDescriptor-Free Design of Multicomponent Catalysts27Using Machine Learning to Predict Oxygen Evolution Activity for Transition Metal Hydroxide Electrocatalysts39High entropy materials based electrocatalysts for water splitting: Synthesis strategies, catalytic mechanisms, and prospects77A machine learning approach for predicting the performance of oxygen carriers in chemical looping oxidative coupling of methane9Machine learning-aided unraveling of the importance of structural features for the electrocatalytic oxygen evolution reaction on multimetal oxides based on their A-site metal configurations7Interpretable design of Ir-free trimetallic electrocatalysts for ammonia oxidation with graph neural networks143Anion Exchange Membrane Water Electrolyzers: An Overview54Predicting and analyzing the fill factor of non-fullerene organic solar cells based on material properties and interpretable machine-learning strategies36Machine 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 transformation239Theoretical study of trimethylamine adsorption on the linear and cyclic polymers and the effect of pore size5Recent Advances in Iridium‐based Electrocatalysts for Acidic Electrolyte Oxidation33Understanding the Performance of (Ni−Fe−Co−Ce)Ox‐Based Water Oxidation Catalysts via Explainable Artificial Intelligence Framework21Prediction and Factor Analysis of Liquefaction Ground Subsidence Based on Machine-Learning Techniques15Machine Learning Approaches to Catalysis0Prediction of Oxygen Evolution Activity for FeCoMn Oxide Catalysts via Machine Learning9Using Machine Learning to Forecast the Conductive Substrate-Supported Heteroatom-Doped Metal Compound Electrocatalysts for Hydrogen Evolution Reaction5Forecasting the Mass Activity of Platinum Anode Catalysts on Various Carbon Nanostructures in Direct Methanol Fuel Cells Using Machine Learning1Machine Learning-Assisted Activation of Lattice Oxygen in Fe-Co3o4 Catalysts for Boosting Electrocatalytic Oxygen Evolution Reaction in Acidic Conditions0Electrocatalysts for Hydrogen Production – Catalyst Design Strategies Based on Crystal Structures of Multimetal Oxides11Parametric Optimization of Transition Metal-Based Nanocomposite Electrocatalysts for Oxygen Evolution Reaction in Alkaline Media4Machine learning informed rational design of high entropy double perovskite oxide universal air/steam electrodes for solid oxide electrochemical cells15Nitrogen -doped NiCoFe oxides hierarchical nanosphere as bifunctional catalysts for overall water splitting1Tuning the O p band center in Co3O4 catalysts by Fe doping for Switching lattice Oxygen-Mediated mechanism in Acidic oxygen evolution reaction10From prediction to performance: Machine learning-guided discovery and validation of Cu-doped Co3O4 for enhanced oxygen evolution reaction2Digital materials ecosystem: from databases to AI agents for autonomous discovery27Rational design of single-atom catalysts stabilized within the interlayer of covalent organic frameworks for the oxygen evolution reaction3Discovery of High-Performance Ni 0.62 Fe 0.23 Ce 0.15 O Electrocatalyst for Oxygen Evolution Reaction via Active Machine Learning8Redox-Durable Co–Ni–Fe Layered Double Hydroxide Anode for Stable Oxygen Evolution under Industrially Relevant Cycling4Material Discovery, Renewable Energy, and the Scope of Shallow Learning and Deep Learning1A dataset for electrocatalytic hydrogen evolution reaction performance of non-noble transition metal phosphides0

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  1. Descriptor-Free Design of Multicomponent Catalysts

    ACS Catalysis · 2022-08-12

    Ying Zhang, Torin C. Peck, Gunugunuri K. Reddy, Debasish Banerjee, Hongfei Jia, Charles A. Roberts, Chen Ling

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

    ACS Applied Materials & Interfaces · 2022-08-31

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

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  3. High entropy materials based electrocatalysts for water splitting: Synthesis strategies, catalytic mechanisms, and prospects

    Nano Research · 2022-12-03

    Xiumin Li, Yifan Zhou, Changrui Feng, Ran Wei, Xiaogang Hao, Keyong Tang, Guoqing Guan

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  4. A machine learning approach for predicting the performance of oxygen carriers in chemical looping oxidative coupling of methane

    Sustainable Energy & Fuels · 2023-01-01

    Dewang Zeng, Yiwen Song, Mengmeng Wang, Yingjie Lu, Zehua Chen, Rui Xiao

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  5. Machine learning-aided unraveling of the importance of structural features for the electrocatalytic oxygen evolution reaction on multimetal oxides based on their A-site metal configurations

    Energy Advances · 2023-01-01

    Yuuki Sugawara, Xiao Chen, Ryusei Higuchi, Takeo Yamaguchi

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  6. Interpretable design of Ir-free trimetallic electrocatalysts for ammonia oxidation with graph neural networks

    Nature Communications · 2023-02-11

    Hemanth Somarajan Pillai, Yi Zhen Li, Shih‐Han Wang, Noushin Omidvar, Qingmin Mu, Luke E. K. Achenie, Frank Abild‐Pedersen, Juan Yang, Gang Wu, Hongliang Xin

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  7. Anion Exchange Membrane Water Electrolyzers: An Overview

    JOURNAL OF CHEMICAL ENGINEERING OF JAPAN · 2023-06-26

    Yuuki Sugawara, Sasidharan Sankar, Shoji Miyanishi, Rajith Illathvalappil, Pranav K. Gangadharan, Hidenori Kuroki, Gopinathan M. Anilkumar, Takeo Yamaguchi

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  8. Predicting and analyzing the fill factor of non-fullerene organic solar cells based on material properties and interpretable machine-learning strategies

    Solar Energy · 2023-12-01

    Min‐Hsuan Lee

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  9. Machine Learning Assisted Prediction of the Mass Activity of Platinum-Based Anode Catalysts Supported on a Variety of Carbon Nanostructures for Direct Methanol Fuel Cells

    SSRN Electronic Journal · 2024-01-01

    Ganesan Raman

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  10. Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation

    Chemical Society Reviews · 2024-01-01

    Rui Fang Ding, Junhong Chen, Yuxin Chen, Jianguo Liu, Yoshio Bando, Xuebin Wang

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  11. Theoretical study of trimethylamine adsorption on the linear and cyclic polymers and the effect of pore size

    Colloids and Surfaces A Physicochemical and Engineering Aspects · 2024-02-08

    Jiyun Cao, Wei Tan, Xiliang Yan, Mingqian Tan, Yuanchao Li

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  12. Recent Advances in Iridium‐based Electrocatalysts for Acidic Electrolyte Oxidation

    ChemSusChem · 2024-02-16

    Wanqing Li, Yunfei Bu, Xinlei Ge, Feng Li, Gao‐Feng Han, Jong‐Beom Baek

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  13. Understanding the Performance of (Ni−Fe−Co−Ce)Ox‐Based Water Oxidation Catalysts via Explainable Artificial Intelligence Framework

    ChemElectroChem · 2024-03-08

    Paul Rossener Regonia, Christian Mark Pelicano

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  14. Prediction and Factor Analysis of Liquefaction Ground Subsidence Based on Machine-Learning Techniques

    Applied Sciences · 2024-03-23

    Kazuki Karimai, Wen Liu, Yoshihisa Maruyama

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  15. Machine Learning Approaches to Catalysis

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    Sachidananda Nayak, Karuthapandi Selvakumar

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  16. Prediction of Oxygen Evolution Activity for FeCoMn Oxide Catalysts via Machine Learning

    Catalysts · 2024-08-08

    Lei Zhang, Jinfei Hou, Honglin Ji, Dan Meng, Jian Qi, Xiaoguang San

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  17. Using Machine Learning to Forecast the Conductive Substrate-Supported Heteroatom-Doped Metal Compound Electrocatalysts for Hydrogen Evolution Reaction

    The Journal of Physical Chemistry C · 2024-10-08

    Nana Zhou, Yaling Zhao, Qingzhang Lv, Yahong Chen

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  18. Forecasting the Mass Activity of Platinum Anode Catalysts on Various Carbon Nanostructures in Direct Methanol Fuel Cells Using Machine Learning

    ECS Advances · 2024-12-02

    Ganesan Raman

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  19. Machine Learning-Assisted Activation of Lattice Oxygen in Fe-Co3o4 Catalysts for Boosting Electrocatalytic Oxygen Evolution Reaction in Acidic Conditions

    SSRN Electronic Journal · 2025-01-01

    Yangshuo Liu, Yaotian Yan, Liang Qiao, Yao Meng, Jiehe Sui, Wei Cai, Xiaohang Zheng

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  20. Electrocatalysts for Hydrogen Production – Catalyst Design Strategies Based on Crystal Structures of Multimetal Oxides

    The Chemical Record · 2025-02-18

    Yuuki Sugawara, Takeo Yamaguchi

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

    Electrocatalysis · 2025-04-11

    Vedasri Bai Khavala, Abhijai Velluva, Adhithyan Kathiravan, Harish Kuruva, Chandan Singh Khavala, Budaraju Srinivasa Murty, Tiju Thomas

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  22. Machine learning informed rational design of high entropy double perovskite oxide universal air/steam electrodes for solid oxide electrochemical cells

    Applied Catalysis B: Environmental · 2025-06-12

    Youdong Kim, Peter Rand, Elliot Brim, Charlie Meisel, Steven R. Goldy, Jayoon Yang, Michael Sanders, Hyun‐Sik Kim, Kanghee Jo, Hee-Soo Lee, Garritt J. Tucker, Cristian V. Ciobanu, Ryan M. Richards, Neal P. Sullivan, Ryan O’Hayre

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  23. Nitrogen -doped NiCoFe oxides hierarchical nanosphere as bifunctional catalysts for overall water splitting

    Journal of Molecular Structure · 2025-10-17

    Min Zhou, Min Zhou, Yang Zhou, Zhuoxun Yin, Xinping Li, Xinping Li, Xinzhi Ma, Jinlong Li, Han Yang

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  24. Tuning the O p band center in Co3O4 catalysts by Fe doping for Switching lattice Oxygen-Mediated mechanism in Acidic oxygen evolution reaction

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    Yangshuo Liu, Yaotian Yan, Feiyu Xu, Liang Qiao, Jiehe Sui, Wei Cai, Xiaohang Zheng

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  25. From prediction to performance: Machine learning-guided discovery and validation of Cu-doped Co3O4 for enhanced oxygen evolution reaction

    International Journal of Hydrogen Energy · 2025-12-27

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  26. Digital materials ecosystem: from databases to AI agents for autonomous discovery

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  27. Rational design of single-atom catalysts stabilized within the interlayer of covalent organic frameworks for the oxygen evolution reaction

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  28. Discovery of High-Performance Ni 0.62 Fe 0.23 Ce 0.15 O Electrocatalyst for Oxygen Evolution Reaction via Active Machine Learning

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  29. Redox-Durable Co–Ni–Fe Layered Double Hydroxide Anode for Stable Oxygen Evolution under Industrially Relevant Cycling

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  30. Material Discovery, Renewable Energy, and the Scope of Shallow Learning and Deep Learning

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  31. A dataset for electrocatalytic hydrogen evolution reaction performance of non-noble transition metal phosphides

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