文献
J-GLOBAL ID:201802254647525507
整理番号:18A1610143
機械学習による気相反応の活性化エネルギー予測の実現可能性【JST・京大機械翻訳】
Feasibility of Activation Energy Prediction of Gas-Phase Reactions by Machine Learning
著者 (7件):
Choi Sunghwan
(Department of Chemistry, KAIST, 291, Daehak-Ro, Yuseong-gu, Daejeon, 34141, Republic of Korea)
,
Choi Sunghwan
(National Institute of Supercomputing and Network, Korea Institute of Science and Technology Information, 245 Daehak-Ro, Yuseong-gu, Daejeon, 34141, Republic of Korea)
,
Kim Yeonjoon
(Department of Chemistry, KAIST, 291, Daehak-Ro, Yuseong-gu, Daejeon, 34141, Republic of Korea)
,
Kim Jin Woo
(Department of Chemistry, KAIST, 291, Daehak-Ro, Yuseong-gu, Daejeon, 34141, Republic of Korea)
,
Kim Zeehyo
(Department of Chemistry, KAIST, 291, Daehak-Ro, Yuseong-gu, Daejeon, 34141, Republic of Korea)
,
Kim Woo Youn
(Department of Chemistry, KAIST, 291, Daehak-Ro, Yuseong-gu, Daejeon, 34141, Republic of Korea)
,
Kim Woo Youn
(KI for Artificial Intelligence, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea)
資料名:
Chemistry - European Journal
(Chemistry - European Journal)
巻:
24
号:
47
ページ:
12354-12358
発行年:
2018年
JST資料番号:
W0744A
ISSN:
0947-6539
CODEN:
CEUJED
資料種別:
逐次刊行物 (A)
記事区分:
原著論文
発行国:
ドイツ (DEU)
言語:
英語 (EN)