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Data Mining and Machine Learning Tools for Combinatorial Material Science of All‐Oxide Photovoltaic Cells
Author(s) -
Yosipof Abraham,
Nahum Oren E.,
Anderson Assaf Y.,
Barad HannahNoa,
Zaban Arie,
Senderowitz Hanoch
Publication year - 2015
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201400174
Subject(s) - photovoltaic system , workflow , computer science , solar energy , work (physics) , energy transformation , cheminformatics , biochemical engineering , data science , nanotechnology , data mining , process engineering , materials science , chemistry , engineering , database , mechanical engineering , physics , electrical engineering , computational chemistry , thermodynamics
Growth in energy demands, coupled with the need for clean energy, are likely to make solar cells an important part of future energy resources. In particular, cells entirely made of metal oxides (MOs) have the potential to provide clean and affordable energy if their power conversion efficiencies are improved. Such improvements require the development of new MOs which could benefit from combining combinatorial material sciences for producing solar cells libraries with data mining tools to direct synthesis efforts. In this work we developed a data mining workflow and applied it to the analysis of two recently reported solar cell libraries based on Titanium and Copper oxides. Our results demonstrate that QSAR models with good prediction statistics for multiple solar cells properties could be developed and that these models highlight important factors affecting these properties in accord with experimental findings. The resulting models are therefore suitable for designing better solar cells.