Machine Learning for Nanomaterial Discovery and Design

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

In the present study “Machine Learning for Nanomaterials Discovery and Design”, a literature review was implemented using bibliometric analysis to summarize the contributions of few thousands of research articles related to nanomaterials. The main concept/idea of this review is interesting and acceptable. Also, the authors did great work in presenting the outcomes in shape of representative figures and tables. There are some comments that should be addressed by the authors prior to recommending the acceptance and publishing of the work by Machine learning & Knowledge extraction. These comments include:

1. Can the authors highlight the main objectives, scope, and contribution of their work in the last paragraph of the introduction? Instead of having one single sentence, I would recommend having a complete paragraph to help the reader understand the major objectives of the review.
2. The authors did not clarify the motivation and knowledge gaps. There are review studies that investigated the applicability of ML models in nanomaterials design and discovery. What are the main differences between this review and others?
3. For the keywords used in the conceptual axis # 1, based on what criteria did the authors select these specific ML models? There are a lot of other ML models that were not included in the keyword criteria such as linear regression, logistics regression, regression trees, and unsupervised ML modeling.
4. What are the main limitations of the employed framework for collecting the dataset?
5. What are the key outcomes of the bibliometric analysis done in the review? I mean the authors need to allocate a certain sub-section for the insights from the work.
6. Also, what are the recommendations for future research directions? Any review study should assist in guiding the researchers for new research avenues. That is the major contribution of review articles generally.
7. The conclusion section is pretty long, I highly recommend condensing it a little.
8. I would recommend having a graphical abstract to represent the entire idea of the review article.
9. The number of references (60 citations) is pretty limited for a review article, I would recommend including additional previous studies to enrich the quality of the literature review.

 

 

 

Author Response

Please, see attached file.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This work is a comprehensive bibliometric review of machine learning applications in nanomaterials from 2010 to 2025. The dataset (Scopus and Web of Science) is large and well-defined, and the use of a PRISMA workflow is clearly described. The inclusion/exclusion criteria – research articles only written in English, eliminating reviews, conference papers, etc. – are reasonable for the purpose of this study. The tools (Bibliometrix, standard indicators, co-occurrence networks, maps) are appropriate/typical of what researchers use in similar works. The paper is well-written and easy to read, its structure includes descriptive statements, journals, institutions, countries, highly cited papers, keyword clusters, thematic maps; it is logical and complete. The literature is well-cited and up-to-date; the list of highly cited articles and core journals provides a good indication that the most important work is represented. Figures and tables are of high quality. In my opinion, this is a useful and well performed review that can provide an entry point for people working in ML in nanomaterials. I recommend publication in the current form.

Author Response

Please, see attached file.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

The authors present a bibliometric study of the emerging field of machine learning and nanomaterials research by selecting and analyzing the articles published in the field over the last years. They classify the information in four clusters that encompass most of the research results and which, according to them, proves that the machine learning has evolved into a central tool in materials discovery. They conclude with some prospects and predictions about the evolution of the field in the future.

 

The study is interesting and sound to a certain extent. The article is well written and the figure are clear, although in some cases the text cannot be clearly distinguished (see e.g. Figure 8). In my opinion it can be considered for publication, provided the authors address the following issues:

 

- Although the study only considers articles after 2010, there are some of them before that date that could be cited, or at least, be commented briefly in the text.

 

- The results are simply presented without much comment or discussion. For instance, what do the authors think about the distribution of countries, institutions, topics etc.? I am not asking for a full or explanation, which might be difficult to get, but to some discussion and interpretation of most of the findings.

 

- Something similar can be said about the use of tools and the classification in groups that the authors perform without much explanation. For instance, why the tendencies in top publications have been classified in four clusters and what are the criteria to classify such clusters into more or less influential?

 

- It would also be good to explain in more detail some of the techniques use in the classification and clusterization and the determination of some properties, such as e.g. the centrality and density that are plotted in Figure 9.

 

Author Response

Please, see attached file.

Author Response File: Author Response.pdf