文献
J-GLOBAL ID:202202285642490138
整理番号:22A0225353
粒子プロセスにおける凝集の程度を予測するための機械学習と結合した新しい計算アプローチ【JST・京大機械翻訳】
A Novel Computational Approach Coupled with Machine Learning to Predict the Extent of Agglomeration in Particulate Processes
著者 (10件):
Sinha Kushal
(Process Engineering, Process Research and Development, AbbVie Inc., North Chicago, Illinois, USA)
,
Sinha Kushal
(Cross-functional Modeling Forum, AbbVie Inc., North Chicago, Illinois, USA)
,
Murphy Eric
(Process Engineering, Process Research and Development, AbbVie Inc., North Chicago, Illinois, USA)
,
Murphy Eric
(Cross-functional Modeling Forum, AbbVie Inc., North Chicago, Illinois, USA)
,
Kumar Prashant
(Solid State Chemistry, Process Research and Development, AbbVie Inc., North Chicago, Illinois, USA)
,
Kumar Prashant
(ZS Associates, Evanston, Illinois, USA)
,
Springer Kirsten A.
(Process Engineering, Process Research and Development, AbbVie Inc., North Chicago, Illinois, USA)
,
Ho Raimundo
(Solid State Chemistry, Process Research and Development, AbbVie Inc., North Chicago, Illinois, USA)
,
Nere Nandkishor K.
(Process Engineering, Process Research and Development, AbbVie Inc., North Chicago, Illinois, USA)
,
Nere Nandkishor K.
(Cross-functional Modeling Forum, AbbVie Inc., North Chicago, Illinois, USA)
資料名:
AAPS PharmSciTech
(AAPS PharmSciTech)
巻:
23
号:
1
ページ:
18
発行年:
2022年
JST資料番号:
W3978A
ISSN:
1530-9932
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)