文献
J-GLOBAL ID:201902269426197497
整理番号:19A2165714
マルチスケールモデリング,実験および生成的深層学習を用いた電池中間相の逆設計に関する展望【JST・京大機械翻訳】
A perspective on inverse design of battery interphases using multi-scale modelling, experiments and generative deep learning
著者 (7件):
Bhowmik Arghya
(Department of Energy Conversion and Storage, Technical University of Denmark, DK-2800, Kgs. Lyngby, Denmark)
,
Castelli Ivano E.
(Department of Energy Conversion and Storage, Technical University of Denmark, DK-2800, Kgs. Lyngby, Denmark)
,
Garcia-Lastra Juan Maria
(Department of Energy Conversion and Storage, Technical University of Denmark, DK-2800, Kgs. Lyngby, Denmark)
,
Jorgensen Peter Bjorn
(DTU Compute, Technical University of Denmark, DK-2800, Kgs. Lyngby, Denmark)
,
Winther Ole
(DTU Compute, Technical University of Denmark, DK-2800, Kgs. Lyngby, Denmark)
,
Winther Ole
(Department of Biology, University of Copenhagen, DK-2200, Copenhagen, Denmark)
,
Vegge Tejs
(Department of Energy Conversion and Storage, Technical University of Denmark, DK-2800, Kgs. Lyngby, Denmark)
資料名:
Energy Storage Materials
(Energy Storage Materials)
巻:
21
ページ:
446-456
発行年:
2019年
JST資料番号:
W3097A
ISSN:
2405-8297
資料種別:
逐次刊行物 (A)
記事区分:
文献レビュー
発行国:
オランダ (NLD)
言語:
英語 (EN)