This work describes the integration of several data mining and machine learning tools for researching Photovoltaic (PV) solar cells libraries into a unified workflow embedded within a GUI-supported Decision Support System (DSS), named PV Analyzer. The analyzer's workflow is composed of several data analysis components including basic statistical and visualization methods as well as an algorithm for building predictive machine learning models. The analyzer allows for the identification of interesting trends within the libraries, not easily observable using simple bi-parametric correlations. This may lead to new insights into factor affecting solar cells performances with the ultimate goal of designing better solar cells. The analyzer was developed using MATLAB version R2014a and consequently could be easily extended by adding additional tools and algorithms. Furthermore, while in our hands, the analyzer has been primarily used in the area of PV cells, is it equally applicable to the analysis of any other dataset composed of activities as dependent variables and descriptors as independent variables.
Keywords: Data mining; Decision Support System; Machine learning; Material-Informatics; Photovoltaic; QSAR; RANSAC; Solar cell.
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