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Discovering Catalytic Reaction Networks Using Deep Reinforc..:

Tian Lan (186455) ; Qi An (331241)
https://figshare.com/articles/dataset/Discovering_Catalytic_Reaction_Networks_Using_Deep_Reinforcement_Learning_from_First-Principles/16734810. , 2021

Link: https://doi.org/10.1021/jacs.1c08794.s002

RT Journal
T1 <Title>Discovering Catalytic Reaction Networks Using Deep Reinforcement Learning from First-Principles</Title>

UL https://suche.suub.uni-bremen.de/peid=base-ftsmithonian:oai:figshare.com:article_16734810&Exemplar=1&LAN=DE
				
A1 Tian Lan (186455)
A1 Qi An (331241)
YR 2021
K1 Biophysics
K1 Biochemistry
K1 Genetics
K1 Sociology
K1 Biological Sciences not elsewhere classified
K1 Chemical Sciences not elsewhere classified
K1 Information Systems not elsewhere classified
K1 previous study based
K1 new path found
K1 many catalytic reactions
K1 complete reaction path
K1 framework quantitatively transforms
K1 fundamental reaction mechanisms
K1 working mechanisms
K1 chemical reactions
K1 reaction pathways
K1 zero knowledge
K1 severely hindered
K1 quantitative search
K1 outstanding capability
K1 model evolves
K1 looking forward
K1 high complexity
K1 framework using
K1 extreme scarcity
K1 dynamic environment
K1 domain knowledge
K1 corresponding actions
K1 bosch process
K1 active fe
JF https://figshare.com/articles/dataset/Discovering_Catalytic_Reaction_Networks_Using_Deep_Reinforcement_Learning_from_First-Principles/16734810
LK http://dx.doi.org/https://doi.org/10.1021/jacs.1c08794.s002
DO https://doi.org/10.1021/jacs.1c08794.s002
SF ELIB - SuUB Bremen

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