- K. Han, J. F. Li, Z. S. Zhang, C. L. Wang*, J. B. Li*, Y. Zhu, Z. J. Zhu, X. J. Liu, G. Yang, L. K. Pan*, Chemical Engineering Journal DOI: 10.1016/j.cej.2025.170251, Rapidly Evaluating Electrochemical Performance of Transition Metal Disulfides for Lithium Ion Batteries Using Machine Learning Classifier

- G. S. Xu, Y. Li, J. F. Li, J. L. Li*, X. J. Liu, C. L. Wang*, W. J. Mai, G. Yang, L. K. Pan*, Angewandte Chemie International Edition 64, e202511389 (2025), Toward Stable Zinc Anode: An AI-Assisted High-Throughput Screening of Electrolyte Additives for Aqueous Zinc-ion Battery

- Y. Y. Chen, Z. L. Yang, J. X. Wang, K. Han, Z. J. Zhu, C. L. Wang*, M. Xu*, G. Yang, L. K. Pan*, Nanoscale 17, 22890-22897 (2025), A Data-Driven Machine Learning Approach for Predictive Modeling of Transition Metal Dichalcogenide/Carbon Composite Supercapacitor Electrodes

H. Wang Y. Zhu, K. Han, C. L. Wang*, J. F. Li, Y. Q. Li Y. Liu*, G. Yang, L. K. Pan*, Journal of Materials Chemistry A 13, 32427-32437 (2025), A Data-driven Machine Learning Approach for Interpretable Predicting Desalination Stability of Carbon Materials for Capacitive Deionization

- H. Wang, C. L. Wang*, J. F. Li, Y. Q. Li, Y. Liu*, Z. Q. Chen, X. J. Liu, G. Yang, L. K. Pan*, Separation and Purification Technology 376, 134181 (2025), A Data-driven Machine Learning Approach to Predict Desalination Capacities of Faradic Materials for Capacitive Deionization

- Y. Y. Chen, H. Wang, C. L. Wang*, J. Q. Yang, X. J. Liu, Y. C. Ma, Y. F. Yao, G. Yang, M. Xu*, L. K. Pan*, Journal of Colloid and Interface Science 699, 138139 (2025), Machine Learning-Guided Prediction of Energy Storage Performance of Carbon Cathode Materials for Zinc-Ion Hybrid Capacitors

- F. Y. Meng, H. Wang, X. Y. Xuan, Y. Liu*, Y. Q. Li, X. T. Xu*, L. K. Pan*, Materials Horizons 12, 8181-8193 (2025), Chloride ion-capturing La0.7Sr0.3BO3 (B=Fe, Co) perovskite oxide achieving superior performance electrochemical desalination

- G. Bai, Z. H. Wang, H. Wang, C. L. Wang*, X. J. Liu, H. B. Li, G. Yang, M. Xu*, L. K. Pan*, Separation and Purification Technology 373, 133627 (2025), Machine Learning-Guided Exploration of Carbon-Based Photothermal Materials for Solar Evaporation

- H. Wang, Y. Zhu, J. L. Li*, X. J. Liu, Y. C. Ma, Y. F. Yao, J. Zhang*, L. K. Pan*, Journal of Materials Chemistry A 13, 17197-17213 (2025), Machine Learning-Accelerated Exploration on Element Doping-Triggering Material Performance Improvement for Energy Conversion and Storage Applications

- S. H. Fan, Y. Gao*, M. Wu, X. J. Liu*, J. W. Li, L. K. Pan*, F. Z. Xuan*, ACS Applied Nano Materials 8, 10013-10021 (2025), Machine Learning-Assisted Design of Transition Metal-Doped Cobalt Phosphide Electrocatalysts for Hydrogen Evolution

- H. Wang, Y. Q. Li, X. Y. Xuan*, K. Wang*, Y. F. Yao, L. K. Pan*, Environmental Science & Technology 59, 6361-6378 (2025), Machine Learning Accelerated Discovery of Covalent Organic Frameworks for Environmental and Energy Applications

- H. Wang, Y. Q. Li, Y. Liu*, X. T. Xu, T. Lu*, L. K. Pan*, Separation and Purification Technology 354, 129423 (2025), Advancement of capacitive deionization propelled by machine learning approach

- Y. M. Yang, K. Han, J. T. Li, T. Zhang, Z. J. Zhu, L. Su, Z. Y. Han, C. Y. Xu, Y. Lu, L. K. Pan, T. Yang, BMC Pulmonary Medicine 25, 123 (2025), A clinical data-driven machine learning approach for predicting the effectiveness of piperacillin-tazobactam in treating lower respiratory tract infections

- G. S. Xu, M. X. Jiang, J. L. Li*, X. Y. Xuan*, J. B. Li, T. Lu, L. K. Pan*, Energy Storage Materials 72, 103710 (2024), Machine learning-accelerated discovery and design of electrode materials and electrolytes for lithium ion batteries

- H. Wang, M. X. Jiang, G. S. Xu, C. L. Wang*, X. T. Xu, Y. Liu*, Y. Q. Li, T. Lu, G. Yang, L. K. Pan*, Small 20, 2401214 (2024), Machine learning-guided prediction of desalination capacity and rate of porous carbons for capacitive deionization

- M. X. Jiang, Z. H. Yang, T. Lu, X. J. Liu*, J. B. Li, C. L. Wang*, G. Yang, L. K. Pan*, Ceramics International 50, 1079-1086 (2024), Machine Learning Accelerated Study for Predicting the Lattice Constant and Substitution Energy of Metal Doped Titanium Dioxide

- G. S. Xu, Y. J. Zhang, M. X. Jiang, J. L. Li*, H. C. Sun, J. B. Li, T. Lu, C. L. Wang*, G. Yang, L. K. Pan*, Chemical Engineering Journal 476, 146676 (2023), A Machine Learning-Assisted Study on Organic Solvents in Electrolytes for Expanding the Electrochemical Stable Window of Zinc-Ion Batteries

- M. X. Jiang, Y. J. Zhang, Z. H. Yang, H. B. Li, J. L. Li, J. B. Li, T. Lu, C. L. Wang*, G. Yang*, L. K. Pan*, Inorganic Chemistry Frontiers 10, 6646 (2023), A Data-driven Interpretable Method to Predict Capacities of Metal ion Doped TiO2 Anode Materials for Lithium-ion Batteries Using Machine Learning Classifiers