American Chemical Society
co7b00121_si_001.pdf (1.04 MB)

Process-Function Data Mining for the Discovery of Solid-State Iron-Oxide PV

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journal contribution
posted on 2017-11-08, 22:18 authored by Elana Borvick, Assaf Y. Anderson, Hannah-Noa Barad, Maayan Priel, David A. Keller, Adam Ginsburg, Kevin J. Rietwyk, Simcha Meir, Arie Zaban
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.