Recently, the research teams led by Prof. Qiang Sun and Prof. Dingguo Xia from the School of Materials Science and Engineering, Peking University, in collaboration with the teams led by Prof. Xueliang Sun, Prof. Shuo Wang and Prof. Jian Peng from the Eastern Institute of Technology, have conducted research centered on machine learning with tight integration of theoretical prediction and experimental validation, and revealed a new mechanism for lithium-ion transport in cation-disordered rocksalt cathodes synergistically regulated by lattice distortion and cation disorder.
Through machine learning-accelerated multi-scale theoretical simulations, the team first found that lattice distortion is not a passive structural perturbation in conventional percolation models — instead, it actively reshapes the potential energy landscape for lithium-ion migration. It can significantly reduce the energy barriers of 1-TM channels deemed blocked in traditional models, bridge originally isolated 0-TM lithium clusters, and modulate short-range cation ordering to promote the formation of lithium-rich local configurations, thus expanding the long-range percolation network.
On this basis, theoretical calculations were further used to screen and design multi-cation cathodes and guide subsequent material synthesis and characterization. Ultimately, experiments not only verified the theoretically predicted structural features, but also delivered an initial discharge specific capacity of 256.3 mAh g⁻¹, with a deviation of only ~0.5% from the theoretically predicted value of 255.1 mAh g⁻¹, forming a complete closed loop of "mechanism discovery via machine learning theory – theoretical material prediction – targeted experimental verification". The related findings were published in the Journal of the American Chemical Society (JACS) under the title Interplay of Lattice Distortion and Cation Disorder Governs Li-Ion Transport in Cation-Disordered Rocksalt Cathodes.
今天 13:30
