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.

中文翻译：

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用于发现固态氧化铁PV的过程功能数据挖掘

众所周知，数据挖掘工具可用于分析通过高通量方法生成的大型材料数据集。通常，用于分析的描述符是结构描述符，可能难以根据分析结果获得和调整它们。在这篇研究文章中，我们展示了使用沉积工艺参数作为描述符来分析光伏数据集。为了创建数据集，使用氧化铁作为吸收层（使用不同的沉积参数沉积）制造了太阳能电池库，并测量了光伏性能。然后使用遗传程序和逐步回归将数据用于构建模型。这些模型表明应该使用哪些沉积参数来获得具有更高性能的光伏电池。基于模型预测而制造的氧化铁文库显示出比以前的任何文库更高的性能，这表明沉积工艺参数可用于对光伏性能进行建模并产生更高性能的电池。这是使用数据挖掘工具发现和制造高性能光伏材料的有前途的技术。