A Novel Machine Learning Model to Predict the Photo-Degradation Performance of Different Photocatalysts on a Variety of Water Contaminants
- Resource Type
- Authors
- Huichun Zhang; Anna Cristina S. Samia; Zhuoying Jiang; Jiajie Hu; Matthew Tong; Xiong Yu
- Source
- Catalysts
Volume 11
Issue 9
Catalysts, Vol 11, Iss 1107, p 1107 (2021)
- Subject
- Materials science
crystal graphic convolutional neural network
Oxide
TP1-1185
photocatalytic degradation
Machine learning
computer.software_genre
Convolutional neural network
Catalysis
chemistry.chemical_compound
Feature (machine learning)
Physical and Theoretical Chemistry
Photodegradation
Photocatalytic degradation
QD1-999
Water contaminants
Artificial neural network
business.industry
Chemical technology
molecular fingerprint
Chemistry
machine learning
chemistry
Photocatalysis
Artificial intelligence
business
computer
artificial neural network
- Language
- English
- ISSN
- 2073-4344
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 of organic compounds is encoded via digital molecular fingerprints (MF). The encoded features of the photocatalysts and contaminants are input to an artificial neural network (ANN), named as CGCNN-MF-ANN model. The CGCNN-MF-ANN model has achieved a very good prediction of the photocatalytic degradation rate constants by different photocatalysts over a wide range of organic contaminants. The effects of the data training strategy on the ML model performance are compared. The effects of different factors on photocatalytic degradation performance are further evaluated by feature importance analyses. Examples are illustrated on the use of this novel ML model for optimal photocatalyst selection and for assessing other types of photocatalysts for different environmental applications.