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Machine Learning-Assisted Development of Organic Solar Cell..:

Yuta Miyake (11873054) ; Akinori Saeki (1280871)
https://figshare.com/articles/dataset/Machine_Learning-Assisted_Development_of_Organic_Solar_Cell_Materials_Issues_Analyses_and_Outlooks/17430449. , 2021

Link: https://doi.org/10.1021/acs.jpclett.1c03526.s002

RT Journal
T1 <Title>Machine Learning-Assisted Development of Organic Solar Cell Materials: Issues, Analyses, and Outlooks</Title>

UL https://suche.suub.uni-bremen.de/peid=base-ftsmithonian:oai:figshare.com:article_17430449&Exemplar=1&LAN=DE
				
A1 Yuta Miyake (11873054)
A1 Akinori Saeki (1280871)
YR 2021
K1 Biochemistry
K1 Biotechnology
K1 Cancer
K1 Plant Biology
K1 Biological Sciences not elsewhere classified
K1 Information Systems not elsewhere classified
K1 power conversion efficiency
K1 large structural freedom
K1 experimental data set
K1 experimental data )
K1 opvs )
K1 data size
K1 data collection
K1 available data
K1 throughput method
K1 substantially improved
K1 recognition motivates
K1 process optimization
K1 present perspective
K1 prediction accuracy
K1 perspective discusses
K1 outlooks nonfullerene
K1 organic photovoltaics
K1 opv publications
K1 nfa category
K1 mordred descriptors
K1 molecules makes
K1 material design
K1 machine learning
K1 limited resources
K1 explanatory variables
K1 conjugated polymers
JF https://figshare.com/articles/dataset/Machine_Learning-Assisted_Development_of_Organic_Solar_Cell_Materials_Issues_Analyses_and_Outlooks/17430449
LK http://dx.doi.org/https://doi.org/10.1021/acs.jpclett.1c03526.s002
DO https://doi.org/10.1021/acs.jpclett.1c03526.s002
SF ELIB - SuUB Bremen

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