Informatics-aided bandgap engineering for solar materials
Informatics-aided bandgap engineering for solar materials
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DOI：
10.1016/j.commatsci.2013.10.016
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发表时间：
2014-02-01
影响因子：
3.3
通讯作者：
Rajan, Krishna
中科院分区：
文献类型：
--
作者：
Dey, Partha;Bible, Joe;Rajan, Krishna
本文预测了200多个新的黄铜矿化合物的带隙以前未经测试的化学。将普通最小二乘法（OLS）、稀疏偏最小二乘法（SPLS）和弹性网络/最小绝对收缩和选择算子（Lasso）回归方法与粗糙集（RS）和主成分分析（PCA）方法相结合的集成数据挖掘方法用于开发用于带隙预测的稳健的定量结构-活性关系（QSAR）类型模型。回归分析的输出是基于使用与带隙最相关的描述符的模型的新化合物的预测带隙。然后采用特征排序算法来：（i）评估带隙和预测模型中使用的化学描述符之间的联系;以及（ii）理解预测中异常值的原因。本文提供了一个描述符引导的选择策略，以确定新的潜在的黄铜矿化学材料的太阳能电池应用。(C)2013年爱思唯尔B。V.保留所有权利。
This paper predicts the bandgaps of over 200 new chalcopyrite compounds for previously untested chemistries. An ensemble data mining approach involving Ordinary Least Squares (OLS), Sparse Partial Least Squares (SPLS) and Elastic Net/Least Absolute Shrinkage and Selection Operator (Lasso) regression methods coupled to Rough Set (RS) and Principal Component Analysis (PCA) methods was used to develop robust quantitative structure - activity relationship (QSAR) type models for bandgap prediction. The output of the regression analyses is the predicted bandgap for new compounds based on a model using the descriptors most related to bandgap. Feature ranking algorithms were then employed to: (i) assess the connection between bandgap and the chemical descriptors used in the predictive models; and (ii) understand the cause of outliers in the predictions. This paper provides a descriptor guided selection strategy for identifying new potential chalcopyrite chemistries materials for solar cell applications. (C) 2013 Elsevier B. V. All rights reserved.