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
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Computational chemistry for water-splitting electrocatalysis

Licheng Miao, Wenqi Jia, Xuejie Cao and Lifang Jiao

Chem. Soc. Rev., 2024, 53, 2771
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Predicting the excited-state properties of crystalline organic semiconductors using GW+BSE and machine learning

Siyu Gao, Yiqun Luo, Xingyu Liu and Noa Marom

Digital Discovery, 2025, 4, 1306
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Photocatalytic activity of dual defect modified graphitic carbon nitride is robust to tautomerism: machine learning assisted ab initio quantum dynamics

Sraddha Agrawal, Bipeng Wang, Yifan Wu, David Casanova and Oleg V. Prezhdo

Nanoscale, 2024, 16, 8986
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Regulation engineering of the surface and structure of perovskite-based electrocatalysts for the oxygen evolution reaction

Ning Yu, Zhi-Jie Zhang, Yong-Ming Chai and Bin Dong

Mater. Chem. Front., 2023, 7, 4236
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From synthesis to properties: expanding the horizons of machine learning in nanomaterials research

Shanhui Diao, Qiong Wu, Shimei Li, Guochen Xu, Xiangling Ren, Longfei Tan, Guihua Jiang, Peng Song and Xianwei Meng

Mater. Horiz., 2025, 12, 4133
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Proposing explainable descriptors towards enhanced N2 reduction performance on the two-dimensional bismuthine nanosheets modified by p-block element-based electrocatalysts

Dengning Sun, Xiaokun Du, Jing Chen, Tao Ye, Hao Shen, Ruirui Wang, Ying Wang, Guoqiang Liu, Yangyang Wan and Zhongti Sun

Phys. Chem. Chem. Phys., 2025, 27, 13248
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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
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Stacking machine learning models for predicting photophysical properties of iridium complexes

Ding Peng, Lin Liu, Bin-Bin Xie, Lin Shen, Xuebo Chen and Wei-Hai Fang

Journal of Photochemistry and Photobiology A: Chemistry, 2025, 466, 116374
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PAH101: A GW+BSE Dataset of 101 Polycyclic Aromatic Hydrocarbon (PAH) Molecular Crystals

Siyu Gao, Xingyu Liu, Yiqun Luo, Xiaopeng Wang, Kaiji Zhao, Vincent Chang, Bohdan Schatschneider and Noa Marom

Sci Data, 2025, 12
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Recent Progress in High‐Throughput On‐Chip Synthesis, Screening, and Data‐Driven Optimization: Toward an Electrocatalyst Chip for Catalysis Universe Exploration

Zhongjie Huang, Zilong Chen, Jiawen Cheng, Jiaqi Zhang, Shuyi Wang, Tingting Chen, Xiaodan Zhang and Huan Pang

Adv Funct Materials, 2025, 35
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Machine Learning Assisted Material Discovery: A Small Data Approach

Qionghua Zhou, Xinyu Chen and Jinlan Wang

Acc. Mater. Res., 2025, 6, 685
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Discovery and verification of two-dimensional organic–inorganic hybrid perovskites via diagrammatic machine learning model

Qiyuan Zhu, Pengcheng Xu, Tian Lu, Xiaobo Ji, Min Shao, Zhiming Duan and Wencong Lu

Materials & Design, 2024, 238, 112642
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Core-dictated tuning of the performance of amorphous and crystalline TM@Pt and HEA@Pt core-shell nanoparticles catalysts

Ilya V. Chepkasov, Viktor S. Baidyshev and Alexander G. Kvashnin

Materials Today Energy, 2025, 54, 102130
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Accurate prediction of synthesizability and precursors of 3D crystal structures via large language models

Zhilong Song, Shuaihua Lu, Minggang Ju, Qionghua Zhou and Jinlan Wang

Nat Commun, 2025, 16
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Artificial intelligence high-throughput prediction building dataset to enhance the interpretability of hybrid halide perovskite bandgap

Wenning Chen, Jungchul Yun, Doyun Im, Sijia Li, Kelvian T. Mularso, Jihun Nam, Bonghyun Jo, Sangwook Lee and Hyun Suk Jung

Journal of Energy Chemistry, 2025, 109, 649
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Specific surface area (SSA) of perovskites with uncertainty estimation approach

Zied Hosni, Sofiene Achour, Fatma Saadi, Jingru Lin, Jie Sheng and Mohammed Al Qaraghuli

Computational Materials Science, 2025, 249, 113668
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Methods and applications of machine learning in computational design of optoelectronic semiconductors

Xiaoyu Yang, Kun Zhou, Xin He and Lijun Zhang

Sci. China Mater., 2024, 67, 1042
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Machine Learning-Guided Design of High-Entropy FeCoCrMnCu Layered Double Hydroxides for Efficient Oxygen Evolution in Alkaline Media

Chandrasekaran Pitchai, Chao-Fang Huang, Ting-Yu Lo, Hung-Chung Li, Ming-Der Yang and Chih-Ming Chen

ACS Catal., 2026, 16, 4502
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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
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Nature of metal-support interaction for metal catalysts on oxide supports

Tairan Wang, Jianyu Hu, Runhai Ouyang, Yutao Wang, Yi Huang, Sulei Hu and Wei-Xue Li

Science, 2024, 386, 915
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Advances and Challenges in Perovskite Oxide Design for High‐Performance Zinc–Air Batteries: Integrating Experimental Strategies and Machine Learning

Huiyi Geng, Xiaohong Zou, Yi Min, Yunfei Bu and Qian Lu

Adv Funct Materials, 2025, 35
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Active Learning for Accelerated Discovery of Two-Dimensional Magnetic Topological Materials

Zhipeng Cao, Shuaihua Lu, Shijun Yuan, Liang Ma, Qionghua Zhou and Jinlan Wang

Chem. Mater., 2025, 37, 6227
DOI: 10.1021/acs.chemmater.5c00908

Inverse design of promising electrocatalysts for CO2 reduction via generative models and bird swarm algorithm

Zhilong Song, Linfeng Fan, Shuaihua Lu, Chongyi Ling, Qionghua Zhou and Jinlan Wang

Nat Commun, 2025, 16
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Rational Design of Earth‐Abundant Catalysts toward Sustainability

Jinyang Guo, Yousof Haghshenas, Yiran Jiao, Priyank Kumar, Boris I. Yakobson, Ajit Roy, Yan Jiao, Klaus Regenauer‐Lieb, David Nguyen and Zhenhai Xia

Advanced Materials, 2024, 36
DOI: 10.1002/adma.202407102

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

T2MAT (text-to-material): A universal agent for generating material structures with goal properties from a single sentence

Zhilong Song, Shuaihua Lu, Qionghua Zhou and Jinlan Wang

Acta Physico-Chimica Sinica, 2026, 42, 100213
DOI: 10.1016/j.actphy.2025.100213