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RESEARCH PAPER · Chemometrics and Intelligent Laboratory Systems · 2019

Prediction of liquidus temperature for complex electrolyte systems Na3AlF6-AlF3-CaF2-MgF2-Al2O3-KF-LiF based on the machine learning methods

Hui Feng Lu, Xiaojun Hu, Bin Cao, Wanqiu Chai, Feiya Yan

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14 direct citing papers in this saved network. Explore the next layer with second-order citations.

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Prediction of liquidus temperature for complex electrolyte systems Na3AlF6-AlF3-CaF2-MgF2-Al2O3-KF-LiF based on the machine learning methodsOriginalData mining assisted prediction of liquidus temperature for primary crystallization of different electrolyte systems4Ab initio molecular dynamics study on microstructure and diffusion properties of CaF2-2.2NaF-AlF3 system11Artificial Intelligence Applied to Battery Research: Hype or Reality?503Effect of Y2O3 and ZnO co-doping on the densification and properties of magnesium aluminum spinel14Ab Initio Molecular Dynamics Simulation of Microstructure and Transport Properties of K3AlF6-2.2NaF-AlF3 System5Machine Learning Orchestrating the Materials Discovery and Performance Optimization of Redox Flow Battery29Bath Temperature Prediction of Aluminum Reduction Cell Based on Machine Learning Algorithm5A method for forecast and auto pre-eliminate the anode effect based on spectrum analysis in the aluminium electrolytic cell0Comparison of machine-learning models in predicting the liquidus temperature of aluminum cryolitic bath0Intelligent Energy Optimization for Electrolytic Aluminum Using Industrial Data-Driven and Knowledge-Guided Modeling8Data driven intelligent research and development of gel electrolyte: Methods, challenges and cutting-edge trends1Machine learning in electrolyte design: Accelerating discovery and multidimensional optimization for next-generation batteries10AI-Driven Smart Manufacturing in the Aluminum Industry: Current Advances and Future Directions0Study on the Initial Crystallization Temperature of the Na3AlF6-K3AlF6-Li3AlF6-AlF3-KF-Al2O3 Electrolyte System0

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  1. Data mining assisted prediction of liquidus temperature for primary crystallization of different electrolyte systems

    Chemometrics and Intelligent Laboratory Systems · 2019-11-11

    Hui Feng Lu, Xiaojun Hu, Bin Cao, Liang Ma, Wanqiu Chai, Yunchuan Yang

    Read original paper ↗ ·

  2. Ab initio molecular dynamics study on microstructure and diffusion properties of CaF2-2.2NaF-AlF3 system

    Chemical Physics · 2021-07-07

    Can Chen, Jie Li, Ruomu Luo, Hongliang Zhang

    Read original paper ↗ ·

  3. Artificial Intelligence Applied to Battery Research: Hype or Reality?

    Chemical Reviews · 2021-09-16

    Teo Lombardo, Marc Duquesnoy, Hassna El-Bouysidy, Fabian Årén, A. Gallo‐Bueno, Peter Bjørn Jørgensen, Arghya Bhowmik, Arnaud Demortière, Elixabete Ayerbe, Francisco Alcaide, Marine Reynaud, Javier Carrasco, Alexis Grimaud, Chao Zhang, Tejs Vegge, Patrik Johansson, Alejandro A. Franco

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  4. Effect of Y2O3 and ZnO co-doping on the densification and properties of magnesium aluminum spinel

    Ceramics International · 2023-07-13

    Meng Xiu, Changyu Hu, Jianhua Liu, Qianxu Ye

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  5. Ab Initio Molecular Dynamics Simulation of Microstructure and Transport Properties of K3AlF6-2.2NaF-AlF3 System

    Metals · 2023-08-27

    Jingkun Wang, Can Chen, Hongliang Zhang, Jiaqi Li

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  6. Machine Learning Orchestrating the Materials Discovery and Performance Optimization of Redox Flow Battery

    ChemElectroChem · 2024-06-10

    Lina Tang, Puiki Leung, Qian Feng Xu, Cristina Flox

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  7. Bath Temperature Prediction of Aluminum Reduction Cell Based on Machine Learning Algorithm

    Journal of Sustainable Metallurgy · 2025-03-14

    Jingkai Lin, Aimin Liu, Zhaowen Wang, Zhongning Shi, Fengguo Liu

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  8. A method for forecast and auto pre-eliminate the anode effect based on spectrum analysis in the aluminium electrolytic cell

    Research Square · 2025-03-31

    Ji-Song Jon, Gwang-Won Ri

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  9. Comparison of machine-learning models in predicting the liquidus temperature of aluminum cryolitic bath

    Materials Chemistry and Physics · 2025-04-09

    Konstantinos Betsis, Nikolaos E. Karkalos, Anthimos Xenidis

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  10. Intelligent Energy Optimization for Electrolytic Aluminum Using Industrial Data-Driven and Knowledge-Guided Modeling

    Metallurgical and Materials Transactions B · 2025-07-14

    Zhengzheng Lian, Cong Zhang, Chao Liu, Hongliang Zhao, Fengqin Liu, Anrui He, Bo Fan, Xinjie Qi, Xin Yuan Jia, Pengfei Liu, Changke Chen

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  11. Data driven intelligent research and development of gel electrolyte: Methods, challenges and cutting-edge trends

    Green Energy & Environment · 2026-04-01

    Yuanchuan Ren, Renjie Huang, Shiyong Zhao, Xuejun Zhu, Yuhang Lin, Tingfeng Su, Cheng Wang, Nanqi Ren

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  12. Machine learning in electrolyte design: Accelerating discovery and multidimensional optimization for next-generation batteries

    Chem · 2026-04-23

    Siyuan Liu, Dengpan Dong, Zhexi Xiao, Dmitry Bedrov, Kang Xu, Lidan Xing, Weishan Li

    Read original paper ↗ ·

  13. AI-Driven Smart Manufacturing in the Aluminum Industry: Current Advances and Future Directions

    Journal of Sustainable Metallurgy · 2026-06-24

    Yu Wang, Mingzhuang Xie, Changke Chen, Pengfei Liu, Hongliang Zhao, Fengqin Liu

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  14. Study on the Initial Crystallization Temperature of the Na3AlF6-K3AlF6-Li3AlF6-AlF3-KF-Al2O3 Electrolyte System

    Journal of Electronic Materials · 2026-07-16

    Guangping Wang, Jie Li, Jianhua Liu, Zhenhua Yang

    Read original paper ↗ ·
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