Theoretical investigation on catalytic mechanisms of oxygen reduction and carbon monoxide oxidation on the MnNx system

Mingming Luo, Zhao Liang, Mingwei Chen, Chao Liu, Xiaopeng Qi, Shaik Gouse Peera, Juan Liu and Tongxiang Liang

New J. Chem., 2020, 44, 15724
DOI: 10.1039/D0NJ03756G

Theoretical exploration of a single-atom catalyst anchored on β12-borophene for electrochemical nitrate reduction: catalyst screening and mechanistic insight

Yuqing Liu, Junjie Zhao, Ming Zhang, Longteng Qu, Tian Wang, Jian Wu, Zhuoran Xu and Ruzhi Wang

Phys. Chem. Chem. Phys., 2025, 27, 10091
DOI: 10.1039/D5CP00117J

Multimetallic nanostructures for electrocatalytic oxygen evolution reaction in acidic media

Taekyung Kim, Byeongyoon Kim, Taehyun Kwon, Ho Young Kim, Jin Young Kim and Kwangyeol Lee

Mater. Chem. Front., 2021, 5, 4445
DOI: 10.1039/D1QM00138H

The rational design of high-performance graphene-based single-atom electrocatalysts for the ORR using machine learning

Ziqiang Chen, Hexiang Qi, Haohao Wang, Caiwei Yue, Yangqiu Liu, Zuoyin Yang, Min Pu and Ming Lei

Phys. Chem. Chem. Phys., 2023, 25, 18983
DOI: 10.1039/D3CP01224G

Screening copper-based single-atom alloy catalysts for electrochemical nitrogen reduction

Hengzhi Liu and Yang-Gang Wang

Phys. Chem. Chem. Phys., 2026, 28, 323
DOI: 10.1039/D5CP03528G

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

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

Chem. Soc. Rev., 2024, 53, 11390
DOI: 10.1039/D4CS00844H

Unravelling the origin of bifunctional OER/ORR activity for single-atom catalysts supported on C2N by DFT and machine learning

Yiran Ying, Ke Fan, Xin Luo, Jinli Qiao and Haitao Huang

J. Mater. Chem. A, 2021, 9, 16860
DOI: 10.1039/D1TA04256D

Machine learning-driven breakthroughs in water electrolysis and supercapacitors

Diab Khalafallah, Fuming Lai, Hao Huang, Jue Wang, Xiaoqing Wang, Shengfu Tong and Qinfang Zhang

Mater. Chem. Front., 2025, 9, 2322
DOI: 10.1039/D5QM00326A

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

Ran Duan, Lei Sun, Xin Wei, Mengqian Xu, Yuwei Pan, Lei Xiao, Yunjie Lang, Weiqiao Deng and Li Yang

J. Mater. Chem. A, 2026
DOI: 10.1039/D5TA10554D

Identifying key factors of peroxymonosulfate activation on single-atom M–N–C catalysts: a combined density functional theory and machine learning study

Yun Sun, Jiachun Cao, Qianyu Li, Didi Li and Zhimin Ao

J. Mater. Chem. A, 2023, 11, 16586
DOI: 10.1039/D3TA02371K

Transition metal–N4 embedded black phosphorus carbide as a high-performance bifunctional electrocatalyst for ORR/OER

Dachang Chen, Zhiwen Chen, Zhuole Lu, Xiaoxing Zhang, Ju Tang and Chandra Veer Singh

Nanoscale, 2020, 12, 18721
DOI: 10.1039/D0NR03339A

Machine learning for design principles for single atom catalysts towards electrochemical reactions

Mohsen Tamtaji, Hanyu Gao, Md Delowar Hossain, Patrick Ryan Galligan, Hoilun Wong, Zhenjing Liu, Hongwei Liu, Yuting Cai, William A. Goddard and Zhengtang Luo

J. Mater. Chem. A, 2022, 10, 15309
DOI: 10.1039/D2TA02039D

Multifunctional electrocatalysis on transition metal-doped biphenylene: a first-principles and machine learning study of single-atom catalysts for HER, OER, and ORR

Dinesh Kumar Dhanthala Chittibabu and Hsin-Tsung Chen

J. Mater. Chem. A, 2026, 14, 10891
DOI: 10.1039/D5TA10121B

Unraveling active ensembles consisting of clusters and single atoms for oxygen reduction: a synergy of machine learning and DFT calculations

Xinyi Li, Dongxu Jiao, Jingxiang Zhao and Xiao Zhao

Inorg. Chem. Front., 2025, 12, 3704
DOI: 10.1039/D5QI00219B

Paradigm in single-atom electrocatalysts for dinitrogen reduction to ammonia

Srikanth Ponnada, Demudu Babu Gorle, Indu Kumari, Sakkarapalayam Murugesan Senthil Kumar, Mark T. Swihart, Gerardine G. Botte and Rakesh K. Sharma

Mater. Chem. Front., 2023, 7, 6427
DOI: 10.1039/D3QM00269A

Theoretically revealing the activity origin of the hydrogen evolution reaction on carbon-based single-atom catalysts and finding ideal catalysts for water splitting

Yiran Wang, Xiaoya Huang, Hanwei Fu and Jiaxiang Shang

J. Mater. Chem. A, 2022, 10, 24362
DOI: 10.1039/D2TA07167C

Machine learning enabled rational design of atomic catalysts for electrochemical reactions

Lianping Wu and Teng Li

Mater. Chem. Front., 2023, 7, 4445
DOI: 10.1039/D3QM00661A

Rational design of single transition-metal atoms anchored on a PtSe2 monolayer as bifunctional OER/ORR electrocatalysts: a defect chemistry and machine learning study

Shijiao Yue, Dongying Li, Aodi Zhang, Yuli Yan, Hongxia Yan, Zhenzhen Feng and Wentao Wang

J. Mater. Chem. A, 2024, 12, 5451
DOI: 10.1039/D3TA07239H

A theoretical roadmap for the best oxygen reduction activity in two-dimensional transition metal tellurides

Xin Yang, Hanyu Liu, Zexing Qu, Yu Xie and Yanming Ma

Chem. Sci., 2022, 13, 11048
DOI: 10.1039/D2SC03686J

Discovery of all-inorganic lead-free perovskites with high photovoltaic performance via ensemble machine learning

Xia Cai, Yan Li, Jianfei Liu, Hao Zhang, Jianguo Pan and Yiqiang Zhan

Mater. Horiz., 2023, 10, 5288
DOI: 10.1039/D3MH00967J

Unraveling the effect of single atom catalysts on the charging behavior of nonaqueous Mg–CO2 batteries: a combined density functional theory and machine learning approach

Rafiuzzaman Pritom, Rahul Jayan and Md Mahbubul Islam

J. Mater. Chem. A, 2024, 12, 2335
DOI: 10.1039/D3TA06742D

Machine learning models for easily obtainable descriptors of the electrocatalytic properties of Ag–Pd–Ir nanoalloys toward the formate oxidation reaction

Xiaoqing Liu, Fuyi Chen, Wanxuan Zhang, Fanzhe Ma and Peng Xu

Nanoscale, 2025, 17, 2810
DOI: 10.1039/D4NR03735A

Machine learning (ML)-assisted development of 2D green catalysts to support sustainability

Manshu Dhillon, Soumya Mahapatra, Adreeja Basu, Shyam S. Pandey, Manpreet Singh Manna, Shantanu Bhattacharya, Basab Chakraborty, Ajeet Kaushik and Aviru Kumar Basu

Mater. Horiz., 2026, 13, 619
DOI: 10.1039/D5MH01739D

Revealing the oxygen Reduction/Evolution reaction activity origin of Carbon-Nitride-Related Single-Atom catalysts: Quantum chemistry in artificial intelligence

Xuhao Wan, Wei Yu, Huan Niu, Xiting Wang, Zhaofu Zhang and Yuzheng Guo

Chemical Engineering Journal, 2022, 440, 135946
DOI: 10.1016/j.cej.2022.135946

Machine learning-guided design of energy-related catalysts from nanoparticles to single-atom sites

Zhongbao Hu, Zhoukun Wang, Yunyi Peng, Zhen Gao, Chanyuan Jin, Zechao Zhuang and Jian-Feng Li

Commun Chem, 2026, 9
DOI: 10.1038/s42004-026-01967-y

Coupling a Crystal Graph Multilayer Descriptor to Active Learning for Rapid Discovery of 2D Ferromagnetic Semiconductors/Half‐Metals/Metals

Shuaihua Lu, Qionghua Zhou, Yilv Guo, Yehui Zhang, Yilei Wu and Jinlan Wang

Advanced Materials, 2020, 32
DOI: 10.1002/adma.202002658

Experimental and numerical analysis of a catalyst layer in the membrane electrode assembly of polymer electrolyte membrane fuel cells

Toru Uenishi and Rui Imoto

Journal of Power Sources, 2023, 580, 233408
DOI: 10.1016/j.jpowsour.2023.233408

Theoretical insight on the delicate regulation of coordination environments for carbon-nitrogen-supported single-atom metals for OER and ORR

Xiao-Kuan Wu, Junan Gao, Zhao Hong, Zhigang Lei and Jie Zhang

Molecular Catalysis, 2025, 572, 114784
DOI: 10.1016/j.mcat.2024.114784

Theory, properties and engineering of 2D magnetic materials

Shucheng Xing, Jian Zhou, Xuanguang Zhang, Stephen Elliott and Zhimei Sun

Progress in Materials Science, 2023, 132, 101036
DOI: 10.1016/j.pmatsci.2022.101036

Theoretical inspection of TM-P4C single-atom electrocatalysts: High performance for oxygen reduction and evolution reactions

Chaohong Guan, Runxin Ouyang, Hong Zhu and Yangyang Xie

Electrochimica Acta, 2022, 427, 140853
DOI: 10.1016/j.electacta.2022.140853

Interpretable physics-informed machine learning approaches to accelerate electrocatalyst development

Hao Wu, Mingxuan Chen, Hao Cheng, Tong Yang, Minggang Zeng and Ming Yang

J. Mater. Inf., 2025, 5
DOI: 10.20517/jmi.2024.67

An application of graph neural network in pollution abatement: Acceleration heterogeneous catalyst design

Qianhong Qiu and Changming Du

Materials Today Communications, 2024, 40, 109916
DOI: 10.1016/j.mtcomm.2024.109916

From prediction to design: Recent advances in machine learning for the study of 2D materials

Hua He, Yuhua Wang, Yajuan Qi, Zichao Xu, Yue Li and Yumei Wang

Nano Energy, 2023, 118, 108965
DOI: 10.1016/j.nanoen.2023.108965

Tuning OER Electrocatalysts toward LOM Pathway through the Lens of Multi-Descriptor Feature Selection by Artificial Intelligence-Based Approach

Haruna Adamu, Sani Isah Abba, Paul Betiang Anyin, Yusuf Sani, Zain Hassan Yamani and Mohammad Qamar

ACS Materials Lett., 2023, 5, 299
DOI: 10.1021/acsmaterialslett.2c00734

Advancements in High‐Throughput Screening and Machine Learning Design for 2D Ferromagnetism: A Comprehensive Review

Chao Xin, Bingqian Song, Guangyong Jin, Yongli Song and Feng Pan

Advcd Theory and Sims, 2023, 6
DOI: 10.1002/adts.202300475

The importance of surface coverages in the rational design of electrocatalysts

Anna Ciotti and Max García-Melchor

Current Opinion in Electrochemistry, 2023, 42, 101402
DOI: 10.1016/j.coelec.2023.101402

MatGPT: A Vane of Materials Informatics from Past, Present, to Future

Zhilong Wang, An Chen, Kehao Tao, Yanqiang Han and Jinjin Li

Advanced Materials, 2024, 36
DOI: 10.1002/adma.202306733

Utilizing High Coordination Diversity in Carbon Nanocone Supported Catalytic Single-Atom Sites for Screening of Optimal Activity

Linjie Deng, Xiyu Li, Guozhen Zhang, Qiquan Luo, Li Yang and Jun Jiang

J. Phys. Chem. Lett., 2022, 13, 7043
DOI: 10.1021/acs.jpclett.2c01398

Discovery of Lead‐Free Perovskites for High‐Performance Solar Cells via Machine Learning: Ultrabroadband Absorption, Low Radiative Combination, and Enhanced Thermal Conductivities

Xia Cai, Yiming Zhang, Zejiao Shi, Ying Chen, Yujie Xia, Anran Yu, Yuanfeng Xu, Fengxian Xie, Hezhu Shao, Heyuan Zhu, Desheng Fu, Yiqiang Zhan and Hao Zhang

Advanced Science, 2022, 9
DOI: 10.1002/advs.202103648

Modulating the Local Coordination Environment of Single‐Atom Catalysts for Enhanced Catalytic Performance in Hydrogen/Oxygen Evolution Reaction

Gracita M. Tomboc, Taekyung Kim, Sangmin Jung, Hyo Jae Yoon and Kwangyeol Lee

Small, 2022, 18
DOI: 10.1002/smll.202105680

Recent progress towards a universal machine learning model for reaction energetics in heterogeneous catalysis

Gloria A Sulley and Matthew M Montemore

Current Opinion in Chemical Engineering, 2022, 36, 100821
DOI: 10.1016/j.coche.2022.100821

High‐Throughput Screening of Electrocatalysts for Nitrogen Reduction Reactions Accelerated by Interpretable Intrinsic Descriptor

Xiaoyun Lin, Yongtao Wang, Xin Chang, Shiyu Zhen, Zhi‐Jian Zhao and Jinlong Gong

Angew Chem Int Ed, 2023, 62
DOI: 10.1002/anie.202300122

Non-noble metal single atom catalysts for electrochemical energy conversion reactions

Sang Eon Jun, Sungkyun Choi, Jaehyun Kim, Ki Chang Kwon, Sun Hwa Park and Ho Won Jang

Chinese Journal of Catalysis, 2023, 50, 195
DOI: 10.1016/S1872-2067(23)64456-0

Machine learning applications on proton exchange membrane water electrolyzers: A component-level overview

Abdelmola Albadwi, Saltuk Buğra Selçuklu and Mehmet Fatih Kaya

International Journal of Hydrogen Energy, 2024, 94, 806
DOI: 10.1016/j.ijhydene.2024.11.188

Hybrid Double Atom Catalysts for Hydrogen Evolution Reaction: A Sweet Marriage of Metal and Nonmetal

Lihong Zhang, Xiangyu Guo, Shengli Zhang, Thomas Frauenheim and Shiping Huang

Advanced Energy Materials, 2024, 14
DOI: 10.1002/aenm.202302754

Machine Learning-Assisted High-Throughput Screening for Electrocatalytic Hydrogen Evolution Reaction

Guohao Yin, Haiyan Zhu, Shanlin Chen, Tingting Li, Chou Wu, Shaobo Jia, Jianxiao Shang, Zhequn Ren, Tianhao Ding and Yawei Li

Molecules, 2025, 30, 759
DOI: 10.3390/molecules30040759

Data‐Driven High‐Throughput Rational Design of Double‐Atom Catalysts for Oxygen Evolution and Reduction

Lianping Wu, Tian Guo and Teng Li

Adv Funct Materials, 2022, 32
DOI: 10.1002/adfm.202203439

High-Throughput Screening of Gas Sensor Materials for Decomposition Products of Eco-Friendly Insulation Medium by Machine Learning

Xuhao Wan, Wei Yu, Anyang Wang, Xiting Wang, John Robertson, Zhaofu Zhang and Yuzheng Guo

ACS Sens., 2023, 8, 2319
DOI: 10.1021/acssensors.3c00376

Building up a general selection strategy and catalytic performance prediction expressions of heteronuclear double-atom catalysts for N2 reduction

Yibo Wu, Cheng He and Wenxue Zhang

Journal of Energy Chemistry, 2023, 82, 375
DOI: 10.1016/j.jechem.2023.03.024

Recent advances in developing multiscale descriptor approach for the design of oxygen redox electrocatalysts

Dantong Zhang, Qi Zhang, Chao Peng, Zhi Long, Guilin Zhuang, Denis Kramer, Sridhar Komarneni, Chunyi Zhi and Dongfeng Xue

iScience, 2023, 26, 106624
DOI: 10.1016/j.isci.2023.106624

Multi‐Task Mixture Density Graph Neural Networks for Predicting Catalyst Performance

Chen Liang, Bowen Wang, Shaogang Hao, Guangyong Chen, Pheng‐Ann Heng and Xiaolong Zou

Adv Funct Materials, 2024, 34
DOI: 10.1002/adfm.202404392

Tailoring Bond Microenvironments and Reaction Pathways of Single‐Atom Catalysts for Efficient Water Electrolysis

Weiqiong Zheng, Ran Zhu, Huijuan Wu, Tian Ma, Hongju Zhou, Mi Zhou, Chao He, Xikui Liu, Shuang Li and Chong Cheng

Angewandte Chemie, 2022, 134
DOI: 10.1002/ange.202208667

Data-driven design and controllable synthesis of Pt/carbon electrocatalysts for H2 evolution

Anhui Zheng, Yuxuan Wang, Fangfei Zhang, Chunnian He, Shan Zhu and Naiqin Zhao

iScience, 2021, 24, 103430
DOI: 10.1016/j.isci.2021.103430

Artificial intelligence in materials science and engineering: Current landscape, key challenges, and future trajectories

Iman Peivaste, Salim Belouettar, Francesco Mercuri, Nicholas Fantuzzi, Hamidreza Dehghani, Razie Izadi, Halliru Ibrahim, Jakub Lengiewicz, Maël Belouettar-Mathis, Kouider Bendine, Ahmed Makradi, Martin Horsch, Peter Klein, Mohamed El Hachemi, Heinz A. Preisig, Yacine Rezgui, Natalia Konchakova and Ali Daouadji

Composite Structures, 2025, 372, 119419
DOI: 10.1016/j.compstruct.2025.119419

Design of Single Atom Catalysts

Zuran Yu, Haoxiang Xu and Daojian Cheng

Advances in Physics: X, 2021, 6
DOI: 10.1080/23746149.2021.1905545

Theoretical Understandings of Graphene-based Metal Single-Atom Catalysts: Stability and Catalytic Performance

Hong-Ying Zhuo, Xin Zhang, Jin-Xia Liang, Qi Yu, Hai Xiao and Jun Li

Chem. Rev., 2020, 120, 12315
DOI: 10.1021/acs.chemrev.0c00818

Sustainable carbon materials in environmental and energy applications

Zhenli Sun, Yun Liao, Yiyan Zhang, Shurui Sun, Qihui Kan, Zhiyao Wu, Long Yu, Zhimin Dong, Zhe Wang, Rong He, Lu Wang, Qi Meng, Hongqing Wang, Qizhao Wang, Liang Mao, Duoqiang Pan, Suhua Wang, Zhibin Zhang, Wenkun Zhu, Shuang Liu, Muhammad Wakeel, Baowei Hu, Tao Duan, Xishi Tai and Xiangke Wang

S, 2025, 1, 0
DOI: 10.48130/scm-0025-0002

DFT calculations and machine learning for the study of ethane dehydrogenation on the heteroatom-doped graphene supported Pt SACs

Yajie Cao, Baojun Wang, Maohong Fan, Lixia Ling and Riguang Zhang

Chemical Engineering Science, 2025, 305, 121169
DOI: 10.1016/j.ces.2024.121169

Tuning the Coordination Environment of Carbon‐Based Single‐Atom Catalysts via Doping with Multiple Heteroatoms and Their Applications in Electrocatalysis

Zhijie Qi, Yan Zhou, Runnan Guan, Yongsheng Fu and Jong‐Beom Baek

Advanced Materials, 2023, 35
DOI: 10.1002/adma.202210575

Curvature effect on graphene-based Co/Ni single-atom catalysts

Shuaihao Tang, Liang Xu, Kejun Dong, Qichen Wang, Jian Zeng, Xin Huang, Haotian Li, Libin Xia and Lingling Wang

Applied Surface Science, 2023, 615, 156357
DOI: 10.1016/j.apsusc.2023.156357

Machine learning utilized for the development of proton exchange membrane electrolyzers

Rui Ding, Yawen Chen, Zhiyan Rui, Kang Hua, Yongkang Wu, Xiaoke Li, Xiao Duan, Jia Li, Xuebin Wang and Jianguo Liu

Journal of Power Sources, 2023, 556, 232389
DOI: 10.1016/j.jpowsour.2022.232389

Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction

Jin Li, Naiteng Wu, Jian Zhang, Hong-Hui Wu, Kunming Pan, Yingxue Wang, Guilong Liu, Xianming Liu, Zhenpeng Yao and Qiaobao Zhang

Nano-Micro Lett., 2023, 15
DOI: 10.1007/s40820-023-01192-5

Gradient Boosting Algorithm for Current-Voltage Prediction of Fuel Cells

Jihyeon Park and Jaeyoung Lee

SSRN Journal, 2022
DOI: 10.2139/ssrn.4110016

Rational Design of Atomically Dispersed Metal Site Electrocatalysts for Oxygen Reduction Reaction

Kechuang Wan, Tiankuo Chu, Bing Li, Pingwen Ming and Cunman Zhang

Advanced Science, 2023, 10
DOI: 10.1002/advs.202203391

Complimentary Computational Cues for Water Electrocatalysis: A DFT and ML Perspective

Ahmed Badreldin, Othmane Bouhali and Ahmed Abdel‐Wahab

Adv Funct Materials, 2024, 34
DOI: 10.1002/adfm.202312425

Coordination Engineering of Single‐Atom Catalysts for the Oxygen Reduction Reaction: A Review

Jincheng Zhang, Hongbin Yang and Bin Liu

Advanced Energy Materials, 2021, 11
DOI: 10.1002/aenm.202002473

Mechanistic understanding on effect of doping nitrogen with graphene supported single-atom Ir toward HER and OER: A computational consideration

Xu Liu, Zhao Jiang and Yang Guo

Chemical Physics Letters, 2024, 834, 140971
DOI: 10.1016/j.cplett.2023.140971

Optimization of cathode catalyst layer of membrane electrode assembly for polymer electrolyte fuel cells using machine learning

Toru Uenishi and Rui Imoto

Journal of Power Sources, 2023, 573, 233105
DOI: 10.1016/j.jpowsour.2023.233105

NiX (X= Co, Cr, Cu, Fe, Ni, Mn, Ti, V) co-doped N6-graphene as efficient trifunctional electrocatalyst for the HER, OER and ORR: A density functional theory study

Meiling Hou, Zhiwei Xiong, Jiayi Li, Xiaoyan Liu, Yu Meng and Xin Zhang

International Journal of Hydrogen Energy, 2026, 202, 153029
DOI: 10.1016/j.ijhydene.2025.153029

Transforming main-group elements into highly active single-atom electrocatalysts for oxygen reduction reactions

Qi Sun, Chengyi Zhang, Yu Mao, Ruihu Lu, Qiu He and Yan Zhao

Electrochimica Acta, 2023, 469, 143223
DOI: 10.1016/j.electacta.2023.143223

Single‐Atom Heterogeneous Catalysts: Human‐ and AI‐Driven Platform for Augmented Designs, Analytics and Reality‐Enabled Manufacturing

Lei Cheng, Yawen Tang, Kostya (Ken) Ostrikov and Quanjun Xiang

Angewandte Chemie, 2024, 136
DOI: 10.1002/ange.202313599

First‐Row Transition Metals for Catalyzing Oxygen Redox

Hengwei Wang, Yu Pei, Keliang Wang, Yayu Zuo, Manhui Wei, Jianyin Xiong, Pengfei Zhang, Zhuo Chen, Nuo Shang, Daiyuan Zhong and Pucheng Pei

Small, 2023, 19
DOI: 10.1002/smll.202304863

Accelerated Discovery of Single‐Atom Catalysts for Nitrogen Fixation via Machine Learning

Sheng Zhang, Shuaihua Lu, Peng Zhang, Jianxiong Tian, Li Shi, Chongyi Ling, Qionghua Zhou and Jinlan Wang

Energy & Environ Materials, 2023, 6
DOI: 10.1002/eem2.12304

Machine Learning Descriptors for Data‐Driven Catalysis Study

Li‐Hui Mou, TianTian Han, Pieter E. S. Smith, Edward Sharman and Jun Jiang

Advanced Science, 2023, 10
DOI: 10.1002/advs.202301020

Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale

Xiang Chen, Xinyan Liu, Xin Shen and Qiang Zhang

Angew Chem Int Ed, 2021, 60, 24354
DOI: 10.1002/anie.202107369

The atomic structural descriptor based on cluster expansion method and superior performance of oxygen reduction on PtTi surface alloys from theoretical perspective

Jia-Qi Tan, Guang-Qiang Yu, Bo-Ying Huang, Wen-jin Yin and Xi-Bo Li

International Journal of Hydrogen Energy, 2024, 59, 359
DOI: 10.1016/j.ijhydene.2024.01.341

Engineering the Local Coordination Environment of Single-Atom Catalysts and Their Applications in Photocatalytic Water Splitting: A Review

Hongli Sun, Yunfei Ma, Qitao Zhang and Chenliang Su

Trans. Tianjin Univ., 2021, 27, 313
DOI: 10.1007/s12209-021-00295-7

Linear and nonlinear machine learning correlation of transition metal cluster characteristics

Alireza Kokabi, Zahra Nasiri Mahd and Zohreh Naghibi

J Nanopart Res, 2021, 23
DOI: 10.1007/s11051-021-05267-5

Theoretical analysis of single Zn atoms with N/C on graphene promoting oxygen redox in alkaline medium

Luya Jin, Kang Wu, Mengqi Liao, Daomiao Wang, Balamurugan Jayaraman, Shaik Gouse Peera and Chao Liu

Molecular Catalysis, 2024, 553, 113799
DOI: 10.1016/j.mcat.2023.113799

Ligands dependent electrocatalytic nitrogen reduction performance in d-π conjugated molecules

Yuxing Lin, Yizhao Feng, Hui Zhou, Ying Han, Hui Sun, Li Shi, Lijuan Meng, Min Zhou, Yongjun Liu and Xiuyun Zhang

Applied Surface Science, 2022, 593, 153338
DOI: 10.1016/j.apsusc.2022.153338

CO2 Adsorptions on d-Block-Metal-Doped Nickel Nanoparticles: Unexpected Adsorption Configurations Predicted by Machine Intelligence

Shiru Lin, Chong Teng and Junwei Lucas Bao

J. Phys. Chem. C, 2021, 125, 19839
DOI: 10.1021/acs.jpcc.1c07133

Recent Progress on Structurally Ordered Materials for Electrocatalysis

Hainan Sun, Sanzhao Song, Xiaomin Xu, Jie Dai, Jie Yu, Wei Zhou, Zongping Shao and WooChul Jung

Advanced Energy Materials, 2021, 11
DOI: 10.1002/aenm.202101937

Auxetic γ-SnO monolayer as a high-performance catalyst for light-driven water splitting and oxygen reduction: Unveiling screening descriptors via machine learning-assisted dimensionality reduction strategy

Renxian Qin, Yali Lu, Xihang Zhang, Yuling Song, Qingjun Zhou and Qiang Zhang

Journal of Power Sources, 2025, 641, 236811
DOI: 10.1016/j.jpowsour.2025.236811

Machine learning-assisted screening of transition metal-doped TMDs for binding energy and charge transfer prediction

Pengfei Jia, Qingbin Zeng, Mingxiang Wang, Yiyi Zhang, Jiefeng Liu, Min Xu and Dachang Chen

Materials Today Communications, 2025, 46, 112603
DOI: 10.1016/j.mtcomm.2025.112603

CdN4C0-gra as efficient trifunctional electrocatalyst for the HER, OER and ORR: A density functional theory study

Luya Jin, Chao Liu, Daomiao Wang, Meiling Liu, Tae Gwan Lee, Shaik Gouse Peera and Xiaopeng Qi

Applied Surface Science, 2023, 610, 155580
DOI: 10.1016/j.apsusc.2022.155580

Machine Learning on a Synergistic Transition Metal Dual‐Atom Surface for Efficient Decomposition of Ammonia

Gaoxiang He, Huihui Yan, Rongli Fan, Mingyue Zhao, Jianming Liu, Yong Zhou, Zhigang Zou and Zhaosheng Li

Small Methods, 2025, 9
DOI: 10.1002/smtd.202500023

High throughput screening of single atomic catalysts with optimized local structures for the electrochemical oxygen reduction by machine learning

Hao Sun, Yizhe Li, Liyao Gao, Mengyao Chang, Xiangrong Jin, Boyuan Li, Qingzhen Xu, Wen Liu, Mingyue Zhou and Xiaoming Sun

Journal of Energy Chemistry, 2023, 81, 349
DOI: 10.1016/j.jechem.2023.02.045

High‐Throughput Screening of Electrocatalysts for Nitrogen Reduction Reactions Accelerated by Interpretable Intrinsic Descriptor

Xiaoyun Lin, Yongtao Wang, Xin Chang, Shiyu Zhen, Zhi‐Jian Zhao and Jinlong Gong

Angewandte Chemie, 2023, 135
DOI: 10.1002/ange.202300122

Theory-oriented screening and discovery of advanced energy transformation materials in electrocatalysis

Hongyu Jing, Peng Zhu, Xiaobo Zheng, Zedong Zhang, Dingsheng Wang and Yadong Li

Advanced Powder Materials, 2022, 1, 100013
DOI: 10.1016/j.apmate.2021.10.004

Click-locking strategy enables automated synthesis of single-atom catalysts with industrial compatibility

Weibin Chen, Long Feng, Bingbing Ma, Lina Zhang, Zhou Du, Fanqi Meng, Shengyao Wang, Shibo Xi, Xiao Hai, Ruiqin Zhong, Jin Zhang, Jiong Lu, Ju Li and Ruqiang Zou

Nat. Synth, 2026
DOI: 10.1038/s44160-026-01004-9

Critical Role of Carbon Substrates in Optimizing Ru‐Based HER Catalysts: From Dimensional Insights to Metal‐Support Interactions Engineering

Shan Zhao, Zheng‐Xiao Li, Hao‐Tian Guo, Jing Li, Zong‐Lin Liu, Peng‐Fei Wang, Lin‐Lin Wang and Ting‐Feng Yi

Adv Funct Materials, 2026, 36
DOI: 10.1002/adfm.202509799

Metal-Doped C3B Monolayer as the Promising Electrocatalyst for Hydrogen/Oxygen Evolution Reaction: A Combined Density Functional Theory and Machine Learning Study

Chen Chen, Bo Xiao, Zhengkun Qin, Jingxiang Zhao, Wenzuo Li, Qingzhong Li and Xuefang Yu

ACS Appl. Mater. Interfaces, 2023, 15, 40538
DOI: 10.1021/acsami.3c07790

Aramid-based highly conductive composite films by incorporating graphene for electromagnetic interference shielding and Joule heating applications

Chunjie Xie, Yuwen Fang, Yuexi Chen, Jing Liu, Zhao-Xia Guo, Xiangyang Hao, Chun Li and Xinlin Tuo

Composites Science and Technology, 2023, 236, 109992
DOI: 10.1016/j.compscitech.2023.109992

An accurate and general local electronic structural descriptor for oxygen reduction on metal surface alloys: Potential of breaking scaling relationship and application to PtZn ones with superior activity

Bo-Ying Huang, Jia-Qi Tan, Cai-Jian Zhang and Xi-Bo Li

Applied Surface Science, 2022, 592, 153325
DOI: 10.1016/j.apsusc.2022.153325

Transition-Metal Interlink Neural Network: Machine Learning of 2D Metal–Organic Frameworks with High Magnetic Anisotropy

Pengju Wang, Jianpei Xing, Xue Jiang and Jijun Zhao

ACS Appl. Mater. Interfaces, 2022, 14, 33726
DOI: 10.1021/acsami.2c08991

Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine Learning

Jianwen Liu, Wenzhi Luo, Lei Wang, Jiujun Zhang, Xian‐Zhu Fu and Jing‐Li Luo

Adv Funct Materials, 2022, 32
DOI: 10.1002/adfm.202110748

Prediction of sintered density of binary W(Mo) alloys using machine learning

He‐Xiong Liu, Yun‐Fei Yang, Yong‐Feng Cai, Chang‐Hao Wang, Chen Lai, Yao‐Wu Hao and Jin‐Shu Wang

Rare Metals, 2023, 42, 2713
DOI: 10.1007/s12598-022-02238-0

Directly predicting N2 electroreduction reaction free energy using interpretable machine learning with non-DFT calculated features

Yaqin Zhang, Yuhang Wang, Ninggui Ma and Jun Fan

Journal of Energy Chemistry, 2024, 97, 139
DOI: 10.1016/j.jechem.2024.05.042

Noble-Metal-Free Multicomponent Nanointegration for Sustainable Energy Conversion

Haijiao Lu, Julie Tournet, Kamran Dastafkan, Yun Liu, Yun Hau Ng, Siva Krishna Karuturi, Chuan Zhao and Zongyou Yin

Chem. Rev., 2021, 121, 10271
DOI: 10.1021/acs.chemrev.0c01328

Machine learning-assisted prediction and optimization of electrocatalytic activity

Huaxin Song, Zhipeng Sun, Yilin Hao, Xiangfen Jiang and Xuebin Wang

Journal of Materials Science & Technology, 2026, 245, 17
DOI: 10.1016/j.jmst.2025.04.050

Catalysis in the digital age: Unlocking the power of data with machine learning

Bokinala Moses Abraham, Mullapudi V. Jyothirmai, Priyanka Sinha, Francesc Viñes, Jayant K. Singh and Francesc Illas

WIREs Comput Mol Sci, 2024, 14
DOI: 10.1002/wcms.1730

Theoretical investigation on the bifunctional hydrogen/oxygen electrode catalysis in Cd–N–C-doped graphene systems regulated by the active center configuration

Chao Liu, Luya Jin, Meiling Liu, Daomiao Wang, Tao Xu, Arunchander Asokan, Balamurugan Jayaraman and Shaik Gouse Peera

International Journal of Hydrogen Energy, 2024, 50, 351
DOI: 10.1016/j.ijhydene.2023.08.139

Machine learning screening of high-performance single-atom electrocatalysts for two-electron oxygen reduction reaction

Xuqian Zhang, Jiming Liu, Rui Li, Xuan Jian, Xiaoming Gao, Zhongli Lu and Xiuping Yue

Journal of Colloid and Interface Science, 2023, 645, 956
DOI: 10.1016/j.jcis.2023.05.011

Rational design of Pt-anchored single-atom alloy electrocatalysts for NO-to-NH3 conversion by density functional theory and machine learning

Jieyu Liu, Haiqiang Guo, Yulin Xiong, Xing Chen, Yifu Yu and Changhong Wang

Chinese Journal of Catalysis, 2024, 62, 243
DOI: 10.1016/S1872-2067(24)60078-1

Machine learning accelerates the screening of single-atom catalysts towards CO2 electroreduction

Yaxin Shi and Zhiqin Liang

Applied Catalysis A: General, 2024, 676, 119674
DOI: 10.1016/j.apcata.2024.119674

Advanced theoretical modeling methodologies for electrocatalyst design in sustainable energy conversion

Tianyi Wang, Qilong Wu, Yun Han, Zhongyuan Guo, Jun Chen and Chuangwei Liu

Applied Physics Reviews, 2025, 12
DOI: 10.1063/5.0235572

Experimental and Theoretical Advances on Single Atom and Atomic Cluster‐Decorated Low‐Dimensional Platforms towards Superior Electrocatalysts

Tianwei He, Alain R. Puente‐Santiago, Shiyu Xia, Md Ariful Ahsan, Guobao Xu and Rafael Luque

Advanced Energy Materials, 2022, 12
DOI: 10.1002/aenm.202200493

Computational screening of transition metal anchored C9N10 as the highly active and selective electrocatalysts for NH3 synthesis

Yibo Chen, Pengyue Zhang, Xiaoyin Liu and Xinyu Zhang

International Journal of Hydrogen Energy, 2024, 62, 673
DOI: 10.1016/j.ijhydene.2024.02.317

Tailoring Bond Microenvironments and Reaction Pathways of Single‐Atom Catalysts for Efficient Water Electrolysis

Weiqiong Zheng, Ran Zhu, Huijuan Wu, Tian Ma, Hongju Zhou, Mi Zhou, Chao He, Xikui Liu, Shuang Li and Chong Cheng

Angew Chem Int Ed, 2022, 61
DOI: 10.1002/anie.202208667

Single‐Atom Heterogeneous Catalysts: Human‐ and AI‐Driven Platform for Augmented Designs, Analytics and Reality‐Enabled Manufacturing

Lei Cheng, Yawen Tang, Kostya (Ken) Ostrikov and Quanjun Xiang

Angew Chem Int Ed, 2024, 63
DOI: 10.1002/anie.202313599

Theoretical Calculation Assisted by Machine Learning Accelerate Optimal Electrocatalyst Finding for Hydrogen Evolution Reaction

Yuefei Zhang, Xuefei Liu and Wentao Wang

ChemElectroChem, 2024, 11
DOI: 10.1002/celc.202400084

A machine learning model with minimize feature parameters for multi-type hydrogen evolution catalyst prediction

Chao Wang, Bing Wang, Changhao Wang, Aojian Li, Zhipeng Chang and Ruzhi Wang

npj Comput Mater, 2025, 11
DOI: 10.1038/s41524-025-01607-4

Machine Learning in Screening High Performance Electrocatalysts for CO2 Reduction

Ning Zhang, Baopeng Yang, Kang Liu, Hongmei Li, Gen Chen, Xiaoqing Qiu, Wenzhang Li, Junhua Hu, Junwei Fu, Yong Jiang, Min Liu and Jinhua Ye

Small Methods, 2021, 5
DOI: 10.1002/smtd.202100987

Encoding the atomic structure for machine learning in materials science

Shunning Li, Yuanji Liu, Dong Chen, Yi Jiang, Zhiwei Nie and Feng Pan

WIREs Comput Mol Sci, 2022, 12
DOI: 10.1002/wcms.1558

Targeted design of advanced electrocatalysts by machine learning

Letian Chen, Xu Zhang, An Chen, Sai Yao, Xu Hu and Zhen Zhou

Chinese Journal of Catalysis, 2022, 43, 11
DOI: 10.1016/S1872-2067(21)63852-4

Machine learning-enabled fast exploration of stable and active single-atom catalysts for oxygen evolution reaction

Woonghyeon Park, Juhwan Noh, Geun Ho Gu, Gunwook Nam, Sang-Mun Jung, Yong-Tae Kim and Yousung Jung

TIMS, 2024, 2, 100072
DOI: 10.59717/j.xinn-mater.2024.100072

Big data-driven machine learning transformation for atomic-scale heterogeneous catalyst design: a critical review

Xiaofan Zheng, Jia Yang, Lianyong Zhou, Ling Qin, Chongjing Lu, Nicolas Alonso-Vante, Zhen Huang, Pengfei Zhang, Yin-Ning Zhou, Zheng-Hong Luo and Li-Tao Zhu

Chemical Engineering Science, 2025, 313, 121740
DOI: 10.1016/j.ces.2025.121740

Feature-Extended Descriptor Construction for Prediction of Consecutive Elementary Reaction Energies in Methane Oxidation

Wangqiang Lin, Fangting Liu, Zhilong Song, Yehui Zhang, Qionghua Zhou, Chongyi Ling, Qiang Li and Jinlan Wang

Chem. Mater., 2025, 37, 4499
DOI: 10.1021/acs.chemmater.5c00930

Machine learning assisted local descriptors predicate oxygen reduction activity of transition metal@Ti1−Zn alloys

Tian-Zhe Wan, Shou-Heng Guo, Guang-Qiang Yu, Jun-Zhe Li, Ya-Nan Zhu and Xi-Bo Li

Computational Materials Science, 2026, 262, 114345
DOI: 10.1016/j.commatsci.2025.114345

Accelerating Optimizing the Design of Carbon‐based Electrocatalyst via Machine Learning

Zhuochen Yu and Weimin Huang

Electroanalysis, 2022, 34, 599
DOI: 10.1002/elan.202100224

Efficient Machine Learning Model Focusing on Active Sites for the Discovery of Bifunctional Oxygen Electrocatalysts in Binary Alloys

Chao Wang, Bing Wang, Changhao Wang, Zhipeng Chang, Mengqi Yang and Ruzhi Wang

ACS Appl. Mater. Interfaces, 2024, 16, 16050
DOI: 10.1021/acsami.3c17377

Coordination environment manipulation of single atom catalysts: Regulation strategies, characterization techniques and applications

Wentao Zhang, Yue Zhao, Wenguang Huang, Tianyin Huang and Bingdang Wu

Coordination Chemistry Reviews, 2024, 515, 215952
DOI: 10.1016/j.ccr.2024.215952

High-Throughput Screening of a Single-Atom Alloy for Electroreduction of Dinitrogen to Ammonia

Guokui Zheng, Yanle Li, Xu Qian, Ge Yao, Ziqi Tian, Xingwang Zhang and Liang Chen

ACS Appl. Mater. Interfaces, 2021, 13, 16336
DOI: 10.1021/acsami.1c01098

A Surrogate Machine Learning Model for the Design of Single-Atom Catalyst on Carbon and Porphyrin Supports towards Electrochemistry

Mohsen Tamtaji, Shuguang Chen, Ziyang Hu, William A. Goddard III and GuanHua Chen

J. Phys. Chem. C, 2023, 127, 9992
DOI: 10.1021/acs.jpcc.3c00765

Catalytic reactivity descriptors of metal‐nitrogen‐doped carbon catalysts for electrocatalysis

Hong Liu, Jiejie Li, Jordi Arbiol, Bo Yang and Pengyi Tang

EcoEnergy, 2023, 1, 154
DOI: 10.1002/ece2.12

Application of machine learning for material prediction and design in the environmental remediation

Yunzhe Zheng, Si Sun, Jiali Liu, Qingyu Zhao, Heng Zhang, Jing Zhang, Peng Zhou, Zhaokun Xiong, Chuan-Shu He and Bo Lai

Chinese Chemical Letters, 2025, 36, 110722
DOI: 10.1016/j.cclet.2024.110722

Understanding the structure-performance relationship of active sites at atomic scale

Runze Li and Dingsheng Wang

Nano Res., 2022, 15, 6888
DOI: 10.1007/s12274-022-4371-x

M-N4-Gr/MXene Heterojunction Nanosheets as Oxygen Reduction and Evolution Reaction Catalysts: Machine Learning and Density Functional Theory Insights

Yunjian Chen, Hong Cui, Qi Jiang, Xue Bai, Pengyue Shan, Zepeng Jia, Sen Lu, Pei Song, Rong Feng, Qin Kang, Zhiyong Liang and Hongkuan Yuan

ACS Appl. Nano Mater., 2023, 6, 7694
DOI: 10.1021/acsanm.3c00851

A Comparative Review: Research in Safety and Sustainability of Carbon Nanomaterials Without and With Machine Learning Assistance

Liqing Wang, Hongyan Wang, Mingyu Bai, Yin Wu, Tongshu Guo, Dirui Cai, Peiyan Sun, Na Xiao, Ansheng Li and Wuyi Ming

IEEE Access, 2024, 12, 167120
DOI: 10.1109/ACCESS.2024.3494549

All Boron Atoms in a ScB12 Monolayer Contribute to the Hydrogen Evolution Reaction

Changyan Zhu, Huimin Chen, Cong Wang, Min Zhang, Yun Geng, Quan Li and Zhongmin Su

J. Phys. Chem. C, 2020, 124, 23221
DOI: 10.1021/acs.jpcc.0c07441

Machine learning aided synthesis and screening of HER catalyst: Present developments and prospects

M Karthikeyan, Durga Madhab Mahapatra, Abdul Syukor Abd Razak, Abdulaziz A.M. Abahussain, Baranitharan Ethiraj and Lakhveer Singh

Catalysis Reviews, 2024, 66, 997
DOI: 10.1080/01614940.2022.2103980

Energy parameter and electronic descriptor for carbon based catalyst predicted using QM/ML

Samadhan Kapse, Shazia Janwari, Umesh V. Waghmare and Ranjit Thapa

Applied Catalysis B: Environmental, 2021, 286, 119866
DOI: 10.1016/j.apcatb.2020.119866

Electrocatalysts for the Oxygen Reduction Reaction in Proton Exchange Membrane Fuel Cells: Significant Advances, Major Challenges, and Future Directions

Sami Mohammed Alfaifi, Rajkamal Balu, Ken Chiang, Namita Roy Choudhury and Naba K. Dutta

ACS Catal., 2025, 15, 9301
DOI: 10.1021/acscatal.5c00903

Machine-learning-assisted insight into the cathode catalyst layer in proton exchange membrane fuel cells

Yuxuan Lou, Mingsheng Hao and Yinshi Li

Journal of Power Sources, 2022, 543, 231827
DOI: 10.1016/j.jpowsour.2022.231827

From density functional theory to machine learning: Emerging paradigms in energy materials discovery

M.V. Jyothirmai, Shrish Nath Upadhyay, S. Muthu Krishnan, Sudipta Roy and Jayant K. Singh

Journal of Power Sources, 2026, 671, 239478
DOI: 10.1016/j.jpowsour.2026.239478

Does Doping Always Increase Activity? Theoretical Insights into Non-metallic Doping Engineering of Corrugated Graphene

Xinying Lin, Qiang Wan and Sen Lin

J. Phys. Chem. Lett., 2025, 16, 2410
DOI: 10.1021/acs.jpclett.5c00088

Rh@C8N8 monolayer as a promising single-atom-catalyst for overall water splitting

Huimin Chen, Changyan Zhu, Chaoxia Wen, Miao Wang, Min Zhang, Yun Geng and Zhongmin Su

Applied Surface Science, 2021, 549, 149320
DOI: 10.1016/j.apsusc.2021.149320

Identifying Molecular Structure–Energy Level Quantitative Relationship of Thermally Activated Delayed Fluorescence Materials Using Machine Learning

Haochen Shi, Yingjie Li, Suling Zhao, Bo Qiao, Zhiqin Liang, Honge Zhao, Zheng Xu and Dandan Song

J. Phys. Chem. C, 2023, 127, 23526
DOI: 10.1021/acs.jpcc.3c07392

Machine learning assisted screening of non-metal doped MXenes catalysts for hydrogen evolution reaction

Mei Yang, Changxin Wang, Minhui Song, Lu Xie, Ping Qian and Yanjing Su

International Journal of Hydrogen Energy, 2025, 113, 740
DOI: 10.1016/j.ijhydene.2025.02.469

Machine-Learning-Accelerated Screening of Double-Atom/Cluster Electrocatalysts for the Oxygen Reduction Reaction

Yuhong Luo, Xiaohang Du, Lanlan Wu, Yanji Wang, Jingde Li and Luis Ricardez-Sandoval

J. Phys. Chem. C, 2023, 127, 20372
DOI: 10.1021/acs.jpcc.3c05753

A first-principles study on the electrochemical reaction activity of 3d transition metal single-atom catalysts in nitrogen-doped graphene: Trends and hints

Caiyan Zheng, Xu Zhang, Zhen Zhou and Zhenpeng Hu

eScience, 2022, 2, 219
DOI: 10.1016/j.esci.2022.02.009

Application of Machine Learning in Optimizing Proton Exchange Membrane Fuel Cells: A Review

Rui Ding, Shiqiao Zhang, Yawen Chen, Zhiyan Rui, Kang Hua, Yongkang Wu, Xiaoke Li, Xiao Duan, Xuebin Wang, Jia Li and Jianguo Liu

Energy and AI, 2022, 9, 100170
DOI: 10.1016/j.egyai.2022.100170

Deep dive into machine learning density functional theory for materials science and chemistry

L. Fiedler, K. Shah, M. Bussmann and A. Cangi

Phys. Rev. Materials, 2022, 6
DOI: 10.1103/PhysRevMaterials.6.040301

Computation of the Thermal Expansion Coefficient of Graphene with Gaussian Approximation Potentials

İlker Demiroğlu, Yenal Karaaslan, Tuğbey Kocabaş, Murat Keçeli, Álvaro Vázquez-Mayagoitia and Cem Sevik

J. Phys. Chem. C, 2021, 125, 14409
DOI: 10.1021/acs.jpcc.1c01888

Review of Carbon Support Coordination Environments for Single Metal Atom Electrocatalysts (SACS)

Wanqing Song, Caixia Xiao, Jia Ding, Zechuan Huang, Xinyi Yang, Tao Zhang, David Mitlin and Wenbin Hu

Advanced Materials, 2024, 36
DOI: 10.1002/adma.202301477

A review of advancements in theoretical simulation of oxygen reduction reaction and oxygen evolution reaction single-atom catalysts

Ninggui Ma, Yu Xiong, Yuhang Wang, Yaqin Zhang, Qianqian Wang, Shuang Luo, Jun Zhao, Changxiong Huang and Jun Fan

Materials Today Sustainability, 2024, 27, 100876
DOI: 10.1016/j.mtsust.2024.100876

Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale

Xiang Chen, Xinyan Liu, Xin Shen and Qiang Zhang

Angewandte Chemie, 2021, 133, 24558
DOI: 10.1002/ange.202107369

Recent advances in local regulation of nickel-based catalysts for electrocatalytic water splitting

Xiaoguang San, Wanmeng Wu, Lei Zhang, Dan Meng, Hui Shao and Jian Qi

International Journal of Hydrogen Energy, 2025, 168, 151097
DOI: 10.1016/j.ijhydene.2025.151097

Prospects of AI in advancing green hydrogen production: From materials to applications

Doudou Zhang, Weisheng Pan, Haijiao Lu, Zhiliang Wang, Bikesh Gupta, Aman Maung Than Oo, Lianzhou Wang, Karsten Reuter, Haobo Li, Yijiao Jiang and Siva Karuturi

Applied Physics Reviews, 2025, 12
DOI: 10.1063/5.0281416

The Future of Catalysis: Applying Graph Neural Networks for Intelligent Catalyst Design

Zhihao Wang, Wentao Li, Siying Wang and Xiaonan Wang

WIREs Comput Mol Sci, 2025, 15
DOI: 10.1002/wcms.70010

Establishing the Principal Descriptor for Electrochemical Urea Production via the Dispersed Dual‐Metals Anchored on the N‐Decorated Graphene

Changyan Zhu, Miao Wang, Chaoxia Wen, Min Zhang, Yun Geng, Guangshan Zhu and Zhongmin Su

Advanced Science, 2022, 9
DOI: 10.1002/advs.202105697

Redox photocatalysis CuFe-BTC for nitrate reduction and formaldehyde oxidation

Xu Huang, Kaiyin Wu, Fei Yang, Ruiyi Fu, Yunfei Ji, Wei Kong, Chao Su and Beibei Xiao

Chemical Engineering Journal, 2025, 504, 158995
DOI: 10.1016/j.cej.2024.158995

Machine Learning-Assisted Design of Transition-Metal-Doped Cobalt Phosphide Electrocatalysts for Hydrogen Evolution

Sihan Fan, Yang Gao, Min Wu, Xinjuan Liu, Jianwei Li, Likun Pan and Fuzhen Xuan

ACS Appl. Nano Mater., 2025, 8, 10013
DOI: 10.1021/acsanm.5c01421

Machine-learning-assisted design of cathode catalysts for metal-sulfur/oxygen/carbon dioxide batteries

Qi Zhang, Rui Yang, Zhengran Wang, Yifan Li, Fangbing Dong, Junjie Liu, Shenglin Xiong, Aimin Zhang and Jinkui Feng

Energy Storage Materials, 2025, 78, 104261
DOI: 10.1016/j.ensm.2025.104261

Tetragonal transition metal sulfides for hydrogen evolution: a density functional theory study

Fei Yang, Yuan Yao, Chao Su, Bei-Bei Xiao and Dai-Fen Chen

Surfaces and Interfaces, 2025, 75, 107819
DOI: 10.1016/j.surfin.2025.107819

Universal Principles for the Rational Design of Single Atom Electrocatalysts? Handle with Care

Giovanni Di Liberto, Luis A. Cipriano and Gianfranco Pacchioni

ACS Catal., 2022, 12, 5846
DOI: 10.1021/acscatal.2c01011

Machine learning-accelerated prediction of overpotential of oxygen evolution reaction of single-atom catalysts

Lianping Wu, Tian Guo and Teng Li

iScience, 2021, 24, 102398
DOI: 10.1016/j.isci.2021.102398

Gradient boosting algorithm for current-voltage prediction of fuel cells

Jihyeon Park and Jaeyoung Lee

Electrochimica Acta, 2022, 432, 141148
DOI: 10.1016/j.electacta.2022.141148

Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery

Haoxin Mai, Tu C. Le, Dehong Chen, David A. Winkler and Rachel A. Caruso

Chem. Rev., 2022, 122, 13478
DOI: 10.1021/acs.chemrev.2c00061