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

Round 1

Reviewer 1 Report

Review of the manuscript Catalysts-1376806- to Authors:

This paper deals with an innovative ML model in order to predict the performance of different metal oxide photocatalysts on a wide range of contaminants. The model, interpretability of the results gained and potential applications were in detail shown and discussed. English language is ok for the most part. I need to just comment on all of the figure 1,2 and 3 on the X axis you wrote predition while I’m guessing you meant prediction? So basically minor revision before acceptance is my suggestion for this article.

Title – Ok.

Abstract – Concise and to the point written, no objections.

Introduction – Offers appropriate information to the readers before the main text.

Results and discussion – The parameters were set good, randomised and repeated. The model achieved reliable predictions. The interpretability of the ML model was also covered quite nicely in this section. The most important application of the model was highlighted.

Materials and methods – Sufficient.

Conclusions – The conclusion section summarizes the key points and results of the paper,

Literature – Ok.

Author Response

Thanks for your supportive comments on this manuscript!

We have corrected the typos in the X axis of Figures 1,2 and 3 on the X axis.   Yes, they should be 'Prediction'.    

Reviewer 2 Report

In this work, authors have nicely developed an innovative machine learning model for predicting the achievement of different metal oxide photocatalysts on a wide range of contaminants. The manuscript is well-writen and all data are consistent with the corresponding conclusions.  From the viewpoint of this reviewer, this issue is a very hot topic and therefore, it can be accepted for publication as is.

Author Response

Thanks for your support!