# Artificial Intelligence Reinventing Materials Engineering: A Bibliometric Review
## 2 Materials and Methods  
To provide an overview of the state of the art of a specific topic, an approach such as bibliometric analysis is deemed appropriate [2]. Additionally, to ensure the integrity and validity of a bibliometric analysis, specific guidelines and approaches should be followed [3,4]. In this study, we adopted the guidelines presented in [4,5] and used the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) protocol to increase reproducibility and accuracy of the documents identified and selected [6]. To carry out this study, the open-source R (v. 4.3.3) package \"Bibliometrix\" (v. 4.2.1) was utilized [5]. To identify documents relevant to the topic, the Scopus and Web of Science (WoS) databases were used due to their high impact, the large number of scientific documents from established sources that they contain, and the ability to extract information that can be used by the Bibliometric tool [7,8]. To identify relevant documents, a detailed query was used: (\"artificial intelligence\" OR \"ai\") AND (\"materials science\" OR \"materials engineer\" OR \"materials science and engineering\" OR \"materials characterization\" OR \"materials test\"). The query used keywords related to AI and material sciences. As the field of AI has undergone drastic advancements in the last few years, only English documents from 2019 to 2023 were searched for at a topic level (e.g., title, abstract, and keywords). As 2024 was still ongoing during the performance of this study, the documents published in 2024 were not included as, by missing data from a whole semester, the outcomes for 2024 would not have been valid. The detailed document processing flowchart is presented in Figure 1.  
Specifically, 889 documents (Scopus: 603 and WoS: 286) that are relevant to the topic were identified in May 2024. The removal of duplicates was performed both automatically through  
Figure 1: Document processing flowchart.  
Bibliometrix as well as manually, and 237 duplicates were found and removed. Hence, the initial screening for eligibility involved 652 documents. Some documents (Reasons 2-7) were removed due to their being proceedings books (10), editorials (10), retracted documents (3), errata (1), and letters (1). Additionally, using the keywords and automatically searching within the titles and abstracts of the documents, 209 documents were removed as they were out of the scope of this study and did not focus on the use of AI in the greater context of materials science. Thereafter, the remaining 418 documents were manually processed and assessed for eligibility through the examination of their full text. The inclusion criteria were for a document to focus on, examine, or use AI or its subfields in materials science, materials engineering, material characterization, or materials testing. Through this process, 148 documents were removed as they did not meet the inclusion criteria. As a result, the document collection examined and analyzed in this study consisted of 270 documents.  
It is worth noting that Scopus and WoS are considered two of the most impactful databases and most appropriate to conduct bibliometric analyses [7; 8], and their indexed documents are highly relevant to the topic. Nonetheless, the use of only two databases can be regarded as a limitation of this study. Additionally, although bibliometric analysis enables us to present a descriptive representation of the current state of the art, it does not allow for an in-depth analysis of each document. To address this, we analyzed the most impactful documents arising from the analysis. Biases can also be introduced in such studies. To counter that, we opted to use the most widely used databases which contain the documents most relevant to the topic, we selected to use a tool widely utilized and specialized for this task, and, in addition, we included all types of documents. Therefore, the metrics presented in this study derive directly from the datasets generated from the two databases.