A Convolutional Neural Network-based gradient boosting framework for prediction of the band gap of photo-active catalysts

A Convolutional Neural Network-based gradient boosting framework for prediction of the band gap of photo-active catalysts

A recent trend in chemical synthesis is photo-catalysis, which uses photo-active catalyst materials that are semiconductor materials. A well-known electronic property of semiconducting materials is the band gap. A photo-catalyst’s desired band gap range is between 1.5 eV and 6.2 eV. A rational desig...

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Bibliographic Details
Main Authors: Avan Kumar, Sreedevi Upadhyayula, Hariprasad Kodamana
Format: Article
Language:English
Published: Elsevier 2023-09-01
Series:Digital Chemical Engineering
Subjects:
Photo-active catalyst
Band gap
Deep learning models
CNN
VGG
Gradient boosting
Online Access:http://www.sciencedirect.com/science/article/pii/S2772508123000273

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http://www.sciencedirect.com/science/article/pii/S2772508123000273

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