Yosipof Abraham, Nahum Oren E, Anderson Assaf Y, Barad Hannah-Noa, Zaban Arie, Senderowitz Hanoch
Department of Chemistry, Bar Ilan University, Ramat-Gan 52900, Israel.
Mol Inform. 2015 Jun;34(6-7):367-79. doi: 10.1002/minf.201400174. Epub 2015 Mar 20.
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.
能源需求的增长，再加上对清洁能源的需求，可能会使太阳能电池成为未来能源资源的重要组成部分。特别是，完全由金属氧化物（MO）制成的电池，如果其功率转换效率得到提高，就有潜力提供清洁且经济实惠的能源。这种改进需要开发新的金属氧化物，这可以通过将组合材料科学用于生产太阳能电池库，并结合数据挖掘工具来指导合成工作而受益。在这项工作中，我们开发了一种数据挖掘工作流程，并将其应用于分析两个最近报道的基于钛和铜氧化物的太阳能电池库。我们的结果表明，可以开发出对多种太阳能电池特性具有良好预测统计的定量构效关系（QSAR）模型，并且这些模型根据实验结果突出了影响这些特性的重要因素。因此，所得模型适用于设计更好的太阳能电池。
