- 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

- 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 DOI: 10.1002/smll.202401214, 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