研究论文
Machine learning-accelerated discovery and design of electrode materials and electrolytes for lithium ion batteries
Guangsheng Xu, Mingxi Jiang, Jinliang Li, Jinliang Li, Xiaoyang Xuan, Jiabao Li, Jiabao Li, Ting Lu 等 9 位
East China Normal University Jinan University Yangzhou University Taishan University
来源Energy storage materials
年份2024
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逐年被引趋势
49250
24
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26
关键指标
84
被引次数 · OpenAlex
7.37
领域内被引倍数
同类平均 = 1
同类平均 = 1
前 0.9%
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同领域 · 同年份 · 同类型
同领域 · 同年份 · 同类型
105
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学术脉络
学科主题
材料 / 化学Machine Learning in Materials Science
Advancements in Battery Materials · Electron and X-Ray Spectroscopy Techniques
参考文献 105
Accelerated Materials Design of Lithium Superionic Conductors Based on First‐Principles Calculations and Machine Learning Algorithms
被引 243Koji Fujimura, Atsuto Seko, Yukinori Koyama · Advanced Energy Materials · 2013
Learning scheme to predict atomic forces and accelerate materials simulations
被引 208Venkatesh Botu, Rampi Ramprasad · Physical Review B · 2015
Rational Molecular Design for Achieving Persistent and Efficient Pure Organic Room-Temperature Phosphorescence
被引 824Weijun Zhao, Zikai He, Jacky W. Y. Lam · Chem · 2016
此处列出前 3 条
引用本文 84
Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials
被引 169Bohayra Mortazavi · Advanced Energy Materials · 2024
Solid-State lithium-ion battery electrolytes: Revolutionizing energy density and safety
被引 81P.U. Nzereogu, A. Oyesanya, S.N. Ogba · Hybrid Advances · 2024
Prospects and challenges of energy storage materials: A comprehensive review
被引 56Md Mir Shakib Ahmed, Md. Jahid Hasan, Md. Shakil Chowdhury · Chemical Engineering Journal Advances · 2024
按被引量排序，此处列出前 3 条