A Novel Machine Learning Model to Predict the Photo-Degradation Performance of Different Photocatalysts on a Variety of Water Contaminants

A Novel Machine Learning Model to Predict the Photo-Degradation Performance of Different Photocatalysts on a Variety of Water Contaminants

This paper describes an innovative machine learning (ML) model to predict the performance of different metal oxide photocatalysts on a wide range of contaminants. The molecular structures of metal oxide photocatalysts are encoded with a crystal graph convolution neural network (CGCNN). The structure...

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Bibliographic Details
Main Authors: Zhuoying Jiang, Jiajie Hu, Matthew Tong, Anna C. Samia, Huichun (Judy) Zhang, Xiong (Bill) Yu
Format: Article
Language:English
Published: MDPI AG 2021-09-01
Series:Catalysts
Subjects:
photocatalytic degradation
machine learning
crystal graphic convolutional neural network
molecular fingerprint
artificial neural network
Online Access:https://www.mdpi.com/2073-4344/11/9/1107

Internet

https://www.mdpi.com/2073-4344/11/9/1107

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