کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1561050 1513926 2014 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Informatics-aided bandgap engineering for solar materials
ترجمه فارسی عنوان
مهندسی باند گاز برای مواد خورشیدی توسط مهندسی اطلاعات
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی مکانیک محاسباتی
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Materials Science - Volume 83, 15 February 2014, Pages 185-195
نویسندگان
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