Papers citing Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine Learning
Deciphering E g Occupancy Descriptor in Oxygen Electrocatalysis
Angewandte Chemie · 2025
Jiaqi Ran, Yuhang Zhang, Shi Xue Dou, Daqiang Gao, Laiquan Li
Read original paper ↗ ·Exploring the Influence of Electronic Structure on Electrocatalytic Nitrate Reduction to Ammonia
Chemistry - An Asian Journal · 2025
Surajit Samui, Sourish Bhattacharya, Ramendra Sundar Dey
Read original paper ↗ ·Rationalizing the d-Band Model from Theory to Practice in Catalyst Design
Journal of the American Chemical Society · 2025
Wenyao Chen, Gang Qian, Haifeng Wang, De Chen, Xinggui Zhou, Weikang Yuan, Xuezhi Duan
Read original paper ↗ ·Data-driven screening of trimetallic electrocatalysts for enhanced water splitting activity
Fuel · 2025
Bo Xiong, Renyuan Li, Chao Yang, Zhipeng Zhao, Xing Zhang, Jing Jing Liu, Lei Zhu
Read original paper ↗ ·Challenges in Scaling Up 2D Material-Based Electrocatalysts
Engineering materials · 2026
K. C. Seetha Lakshmi, Balaraman Vedhanarayanan
Read original paper ↗ ·Engineering the dynamic reactant–electrode interface in electroreduction systems: advances, challenges and prospects
Chemical Society Reviews · 2026
Kouer Zhang, Lizhen Wu, Xiaomin Xu, Zongping Shao, Liang An
Read original paper ↗ ·Machine learning-driven high-throughput screening of electrocatalysts and electrolytes for electrochemical surfaces and interfaces
Chemical Communications · 2026
Shun Zou, Lipan Luo, Guanyu Wang, Haochen Shen, Haoyun Bai, Guobin Wen, Bohua Ren, Shuangyin Wang
Read original paper ↗ ·Machine learning-guided design principles for single-atom catalysts in persulfate-based advanced oxidation processes
Journal of Materials Chemistry A · 2026
Zijun Kong, Huayue Kang, Shibo Gao, Meng Du, Hongda Zhang, Yidi Chen, Nanqi Ren
Read original paper ↗ ·Modulating the electronic properties and hydrogen evolution reaction performance of precious-metal on 2D-SiC via surface defects
Physical Chemistry Chemical Physics · 2026
Wenzhong Li, Xiefei Song, Yuqiong Shan
Read original paper ↗ ·Molecular catalysts for the oxygen reduction reaction based on earth abundant transition metals: progress, limitations, and future opportunities
Chemical Communications · 2026
Soumalya Roy, Sudipta Palit, Sourav Das, Debasis Ghosh
Read original paper ↗ ·Accelerated discovery of MM ′ XT 2 MXenes for catalysis, electronics, and energy storage using supervised machine learning
Materials Today Physics · 2026
Umair Haider, Gul Rahman, Imran Shakir, Mohammed Sultan Al-Buriahi, Norah Alomayrah, Imen Kebaïli
Read original paper ↗ ·Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability
npj Computational Materials · 2026
Rogério Almeida Gouvêa, Pierre-Paul De Breuck, Tatiane Pretto, Gian‐Marco Rignanese, Marcos J. L. Santos
Read original paper ↗ ·Catalytic Ozonation of Volatile Organic Compounds: Reaction Mechanisms, Catalyst Design, and Characterization
ChemCatChem · 2026
Yiyuan Wang, Chenhe Wu, Jimin Wang, Jinhua Ye, Lequan Liu
Read original paper ↗ ·Design, characterization, and dynamic behavior of atomically dispersed Pt catalysts for water splitting and oxygen reduction: From experimental insights to machine-learning-assisted analysis
Chemical Physics Reviews · 2026
Shuai Kang, Wendi Yang, Huan Li, Ziming Zhao, Wenqiang Lu, Longxing Su
Read original paper ↗ ·Precisely tuned CuFe-based multimetallic catalysts: machine learning guided DFT design for ultra-efficient overall water splitting
Applied Surface Science · 2026
Bo Xiong, Tianxiang Li, Xiangjun Wu, Guiqin Zhu, Ruilin Zheng, Jing Liu, Yingju Yang, Zhongpu Fang, Bojie Li, Lei Zhu
Read original paper ↗ ·Accelerating single-atom ORR catalyst discovery through theory and machine learning: a critical review
Coordination Chemistry Reviews · 2026
Anuj Kumar, Parth Bishnoi, Akshay Parmar, Mohammad Khalid, Mohd Ubaidullah, Abdullah M. Al‐Enizi
Read original paper ↗ ·Potential, challenges and opportunities of machine learning model for plasma-based gas conversion: a critical review
ENGINEERING Chemical Engineering · 2026
Jiayin Li, Xinpei Lu, Sirui Li, Annemie Bogaerts
Read original paper ↗ ·Data-driven design of electrocatalysts: Machine learning workflows, paradigm shifts, and applications
Advanced Powder Materials · 2026
Ziwei Liu, Hanqing Gu, Liwei Yang, Wengcheng Wu, Zhong Huang, Haozhi Wang, Yang Wang, Yida Deng
Read original paper ↗ ·Machine Learning for Superconductor Discovery: From Data-Driven Insights to Accelerated Design
ACS Omega · 2026
Jingzi Zhang, Chengquan Zhong, Cailu Xiao, Hao Wu, Weiyan Kong, Zeyun Cai, Jianchao He, H Chen, J W Li, Kailong Hu, Xi Lin, Xuan Wang, Xuesong Leng
Read original paper ↗ ·Combining DFT calculations and machine learning for rational design of hydrogen electrocatalysts
Chinese Journal of Chemical Engineering · 2026
Shuna Yang, HJ Xu, Wenhao Lv, Shaojie Yang, Yawei Liu, Feng Huo
Read original paper ↗ ·AI-guided surface and defect engineering of sustainable carbon electrocatalysts: Descriptor discovery, active-site validation, and scalable electrochemical conversion
Carbon Trends · 2026
Palanivendhan Murugadoss, Ravikumar Jayabal, Ramachandra CG, Sunil Kumar Jakhar, M. Vichitra, Jajneswar Nanda, Priya Parag Saxena, Aseel Smerat, Kamakshi Priya K
Read original paper ↗ ·Machine Learning-Guided Prediction of Low-Barrier N 2 and CO 2 Activation for Catalyst Design
ACS Applied Energy Materials · 2026
Balaraman Vedhanarayanan, Abhinav S. Raman, K. C. Seetha Lakshmi
Read original paper ↗ ·Learning crystal–descriptor synergy: A multimodal deep learning approach for band gap prediction in chalcogenide materials
Physica B Condensed Matter · 2026
Hilal Kaya, Fatih Ekinci, Ahmet Yasin Selçuk, Neşet Koçkar, Özge DEMİR, Güney Ugurlu, Mehmet Serdar Güzel, Mehmet Serdar Güzel
Read original paper ↗ ·From Data to Discovery: Crafting Data‐Driven Frameworks for Advancing OER Electrocatalysts
Advanced Functional Materials · 2026
Lu Jia, Zihao Jin, Yueyang Tan, Zhongyan Zhang, Yayun Pu, Yang‐Gang Wang, Limin Huang
Read original paper ↗ ·How can high-throughput experimentation accelerate the rational design of electro-catalysts for sustainable energy conversion?
Progress in Materials Science · 2026
Johannes D. Bartl, Anat Itzhak, Hannah‐Noa Barad
Read original paper ↗ ·Artificial Intelligence and Machine Learning for Oxygen Reduction Reaction: A Review on Catalyst Discovery and Durability Modeling
Electroanalysis · 2026
Aaditya Sharma, K. Jayachandiran, Shankar Raman Dhanushkodi, Haijiang Wang, Zhongwei Chen
Read original paper ↗ ·Extraordinary Thermal Radiation Suppression Enables High‐Entropy Oxide Ceramics for Ultrahigh‐Temperature Protection up to 2000 K
Advanced Functional Materials · 2026
Enyu Xie, Shuqi Wang, Qiancheng Zhao, Zijian Peng, Jingran Guo, Guoliang Chen, Yongchun Zou, Qingyuan Zhao, Zhiyun Ye, Jingchi Mu, Junteng Yao, Xiang Qun Xu, Jiahu Ouyang, Dechang Jia, Yu Zhou, Yaming Wang
Read original paper ↗ ·