Citation: Ning Chen, Yining Wang, Chunhua Yang, Muyan Xie, Weihua Gui, Zhixing Wang, Jiayao Chen, Jiexi Wang. Quantifying morphology for dynamic grain evolution modeling and property correlation analysis in Ni-rich cathode materials[J]. Chinese Chemical Letters, ;2026, 37(11): 111753. doi: 10.1016/j.cclet.2025.111753 shu

Quantifying morphology for dynamic grain evolution modeling and property correlation analysis in Ni-rich cathode materials

Ning Chen ^a ,
Yining Wang ^a ,
Chunhua Yang ^a ,
Muyan Xie ^a ,
Weihua Gui ^a ,
Zhixing Wang ^b ,
Jiayao Chen ^{a, *,} ,
Jiexi Wang ^{b, *,}

a.
School of Automation, Central South University, Changsha 410083, China
b.
chool of Metallurgy and Environment, Central South University, Changsha 410083, China

jychen@csu.edu.cn

wangjiexikeen@csu.edu.cn

Received Date: 13 June 2025
Revised Date: 11 August 2025
Accepted Date: 25 August 2025
Available Online: 26 August 2025

Figures(6)

During the sintering process of Ni-rich cathode materials, the ultimate morphology of grains significantly impact capacity and cycling performance. However, revealing the quantified morphology variation of grains during the sintering process is challenging. Therefore, this paper proposes a modeling method for dynamic grain morphology evolution of Ni-rich cathode materials and analyzes the morphology-performance relationships. First, based on the morphological evolution laws, an indicator reflecting the degree to which the grain tends to become circle is designed to quantitatively describe the grain morphology, named roundness. Evolution models of grain roundness are established separately according to the characteristics of thermodynamics and microstructural evolution in each stage of sintering. During the heating stage, the mixture density network estimates grain size and roundness distributions, which are then used to generate initial grain distribution images for constant temperature stage modeling. For the constant temperature stage, a cellular automata based on grain growth mechanisms is proposed to simulate dynamic morphological changes in grains. Building upon the above framework, this study constructs a predictive relationship between grain roundness and specific surface area, then investigates the correlation mechanism between grain roundness distribution and electrochemical performance. Simulation results indicate that this method can accurately predict morphological changes during grain growth and provide theoretical guidance for cathode material preparation.
- Ni-rich cathode materials,
- Mixture density network,
- Cellular automata,
- Grain roundness,
- Electrochemical performance
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