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Maximizing the Energy Density and Stability of Ni-Rich Layered Cathode Materials with Multivalent Dopants Via Machine Learning
Minseon Kim, Seungpyo Kang, Hyun Gyu Park, Kwangjin Park, Kyoungmin Min
Soongsil University Gachon University
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学术脉络
学科主题
工程Advancements in Battery Materials
Advanced Memory and Neural Computing · Machine Learning in Materials Science
参考文献 70
Generalized Gradient Approximation Made Simple
被引 217,230John P. Perdew, Kieron Burke, Matthias Ernzerhof · Physical Review Letters · 1996
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
被引 13,280Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier · APL Materials · 2013
Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set
被引 76,641Georg Kresse, Jürgen Furthmüller · Computational Materials Science · 1996
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引用本文 8
Mitigating irreversible phase transition of Y-doped LiNi0.925Co0.03Mn0.045O2 by lattice engineering
被引 17Zhongyuan Luo, Huan Li, Weigang Wang · Ceramics International · 2023
Rapid discovery of promising materials via active learning with multi-objective optimization
被引 13Taehyun Park, Eunsong Kim, Jiwon Sun · Materials Today Communications · 2023
High-throughput screening of Ni-rich layered cathode materials for sodium-ion batteries based on machine learning
被引 8Tian‐E Fan, Haoran Lei, Kai You Wang · Journal of Alloys and Compounds · 2025
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