Undiscovered perovskite materials for applications in capturing solar lights are explored through the implementation of data science. In particular, 15000 perovskite materials data is analyzed where visualization of the data reveals hidden trends and clustering of data. Random forest classification within machine learning is used in order to predict the band gap of perovskite materials where 18 physical descriptors are revealed to determine the band gap. With trained random forest, 9328 perovskite materials with potential for applications in solar cell materials are predicted. The selected Li and Na based perovskite materials within predicted 9328 perovskite materials are evaluated with first principle calculations where 11 undiscovered Li(Na) based perovskite materials fall into the ideal band gap and formation energy ranges for solar cell applications. Thus, the implementation of data science accelerates the discovery of hidden perovskite materials and the approach can be applied to the materials science in general for searching undiscovered materials.

中文翻译：

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结合数据科学和第一性原理计算寻找光伏系统中隐藏的钙钛矿材料

通过实施数据科学，探索了用于捕获太阳能的未发现的钙钛矿材料。特别是，分析了15000个钙钛矿材料数据，其中数据的可视化揭示了隐藏的趋势和数据的聚类。使用机器学习中的随机森林分类来预测钙钛矿材料的带隙，其中揭示了18个物理描述符以确定带隙。通过训练有素的随机森林，可以预测9328种钙钛矿材料在太阳能电池材料中的应用潜力。使用第一原理计算评估预测的9328钙钛矿材料中所选的基于Li和Na的钙钛矿材料，其中11种未发现的Li（Na）钙钛矿材料落入太阳能电池应用的理想带隙和形成能范围内。因此，数据科学的实施加快了隐藏钙钛矿材料的发现，并且该方法通常可以应用于材料科学来搜索未发现的材料。