Synthesis of Lithium-ion Conducting Polymers Designed by Machine Learning-based Prediction and Screening

被引：39

作者：

Hatakeyama-Sato, Kan ^{[1
,2
]}

Tezuka, Toshiki ^{[1
,2
]}

Nishikitani, Yoshinori ^{[1
,2
]}

Nishide, Hiroyuki ^{[1
,2
]}

Oyaizu, Kenichi ^{[1
,2
]}

机构：

[1] Waseda Univ, Dept Appl Chem, Tokyo 1698555, Japan

[2] Waseda Univ, Res Inst Sci & Engn, Tokyo 1698555, Japan

来源：

CHEMISTRY LETTERS | 2019年 / 48卷 / 02期

关键词：

Solid polymer electrolyte; Lithium-ion battery; Machine learning; ELECTROLYTE;

D O I：

10.1246/cl.180847

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

A database for 240 types of lithium-ion conducting solid polymer electrolytes was newly constructed and analyzed by machine learning. Despite the complexity of the polymer composites as electrolytes, accurate prediction was achieved by the appropriate learning model. Inspired by the analyses, poly(glycidyl ether) derivatives were synthesized to yield higher conductivity. Screening of single-ion conducting polymers with de novo design (>15000 candidates) was also conducted based on the established database.

引用

页码：130 / 132

页数：3

共 50 条

[41] Lithium-Ion Battery Health Prediction on Hybrid Vehicles Using Machine Learning Approach
Jafari, Sadiqa
Shahbazi, Zeinab
Byun, Yung-Cheol
ENERGIES, 2022, 15 (13)
[42] Helical Covalent Polymers with Unidirectional Ion Channels as Single Lithium-Ion Conducting Electrolytes
Hu, Yiming
Dunlap, Nathan
Long, Hai
Chen, Hongxuan
Wayment, Lacey J.
Ortiz, Michael
Jin, Yinghua
Nijamudheen, Abdulrahiman
Mendoza-Cortes, Jose L.
Lee, Se-hee
Zhang, Wei
CCS CHEMISTRY, 2021, 3 (12): : 2762 - 2770
[43] Review on Machine Learning Methods for Remaining Useful Lifetime Prediction of Lithium-ion Batteries
Su, Nicholas Kwong Howe
Juwono, Filbert H.
Wong, W. K.
Chew, I. M.
2022 INTERNATIONAL CONFERENCE ON GREEN ENERGY, COMPUTING AND SUSTAINABLE TECHNOLOGY (GECOST), 2022, : 286 - 292
[44] Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium-Ion Batteries
Lv, Chade
Zhou, Xin
Zhong, Lixiang
Yan, Chunshuang
Srinivasan, Madhavi
Seh, Zhi Wei
Liu, Chuntai
Pan, Hongge
Li, Shuzhou
Wen, Yonggang
Yan, Qingyu
ADVANCED MATERIALS, 2022, 34 (25)
[45] Machine learning-based prediction of transfusion
Mitterecker, Andreas
Hofmann, Axel
Trentino, Kevin M.
Lloyd, Adam
Leahy, Michael F.
Schwarzbauer, Karin
Tschoellitsch, Thomas
Boeck, Carl
Hochreiter, Sepp
Meier, Jens
TRANSFUSION, 2020, 60 (09) : 1977 - 1986
[46] A machine-learning prediction method of lithium-ion battery life based on charge process for different applications
Yang, Yixin
APPLIED ENERGY, 2021, 292
[47] Machine learning based swift online capacity prediction of lithium-ion battery through whole cycle life
Xue, Qiao
Li, Junqiu
Xu, Peipei
ENERGY, 2022, 261
[48] Capacity prediction of Lithium-ion batteries based on adaptive sliding window pooling extreme learning machine algorithm
Han, Caiyun
Yuan, Huimei
PHYSICA SCRIPTA, 2024, 99 (01)
[49] Lithium-Ion conducting oxides: Synthesis, structure, and electroconducting properties
E. A. Fortal’nova
O. N. Gavrilenkov
A. G. Belous
E. D. Politova
Russian Journal of General Chemistry, 2009, 79 : 1987 - 1997
[50] Lithium-Ion conducting oxides: Synthesis, structure, and electroconducting properties
Fortal'nova, E. A.
Gavrilenkov, O. N.
Belous, A. G.
Politova, E. D.
RUSSIAN JOURNAL OF GENERAL CHEMISTRY, 2009, 79 (09) : 1987 - 1997

← 1 2 3 4 5 →