Process-Function Data Mining for the Discovery of
Solid-State Iron-Oxide PV
Posted on 2017-11-08 - 22:18
Data
mining tools have been known to be useful for analyzing large
material data sets generated by high-throughput methods. Typically,
the descriptors used for the analysis are structural descriptors,
which can be difficult to obtain and to tune according to the results
of the analysis. In this Research Article, we show the use of deposition
process parameters as descriptors for analysis of a photovoltaics
data set. To create a data set, solar cell libraries were fabricated
using iron oxide as the absorber layer deposited using different deposition
parameters, and the photovoltaic performance was measured. The data
was then used to build models using genetic programing and stepwise
regression. These models showed which deposition parameters should
be used to get photovoltaic cells with higher performance. The iron
oxide library fabricated based on the model predictions showed a higher
performance than any of the previous libraries, which demonstrates
that deposition process parameters can be used to model photovoltaic
performance and lead to higher performing cells. This is a promising
technique toward using data mining tools for discovery and fabrication
of high performance photovoltaic materials.
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Borvick, Elana; Anderson, Assaf Y.; Barad, Hannah-Noa; Priel, Maayan; Keller, David A.; Ginsburg, Adam; et al. (2017). Process-Function Data Mining for the Discovery of
Solid-State Iron-Oxide PV. ACS Publications. Collection. https://doi.org/10.1021/acscombsci.7b00121
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AUTHORS (9)
EB
Elana Borvick
AA
Assaf Y. Anderson
HB
Hannah-Noa Barad
MP
Maayan Priel
DK
David A. Keller
AG
Adam Ginsburg
KR
Kevin J. Rietwyk
SM
Simcha Meir
AZ
Arie Zaban