# Artificial Intelligence Reinventing Materials Engineering: A Bibliometric Review
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Document** & **DOI** & **Total Citations** & **Total Citations per Year** & **Normalized Total Citations** \\ \hline
[10] & [[https://doi.org/10.1039/d0](https://doi.org/10.1039/d0) cs0098a]([https://doi.org/10.1039/d0](https://doi.org/10.1039/d0) cs0098a) & 382 & 76.4 & 6.16 \\
[11] & [[https://doi.org/10.1515/nanoph-2019-0474](https://doi.org/10.1515/nanoph-2019-0474)]([https://doi.org/10.1515/nanoph-2019-0474](https://doi.org/10.1515/nanoph-2019-0474)) & 322 & 64.4 & 5.2 \\
[12] & [[https://doi.org/10.1016/j.xim.2021.100179](https://doi.org/10.1016/j.xim.2021.100179)]([https://doi.org/10.1016/j.xim.2021.100179](https://doi.org/10.1016/j.xim.2021.100179)) & 287 & 71.75 & 6.59 \\
[13] & [[https://doi.org/10.1039/d0](https://doi.org/10.1039/d0) mh01541f]([https://doi.org/10.1039/d0](https://doi.org/10.1039/d0) mh01541f) & 228 & 57 & 5.23 \\
[15] & [[https://doi.org/10.1002/cctc.20190595](https://doi.org/10.1002/cctc.20190595)]([https://doi.org/10.1002/cctc.20190595](https://doi.org/10.1002/cctc.20190595)) & 214 & 35.67 & 3.03 \\
[14] & [[https://doi.org/10.1021/acschemwc00749](https://doi.org/10.1021/acschemwc00749)]([https://doi.org/10.1021/acschemwc00749](https://doi.org/10.1021/acschemwc00749)) & 213 & 53.25 & 4.89 \\
[16] & [[https://doi.org/10.1088/2515-769/ab13](https://doi.org/10.1088/2515-769/ab13) bb]([https://doi.org/10.1088/2515-769/ab13](https://doi.org/10.1088/2515-769/ab13) bb) & 200 & 33.33 & 2.83 \\
[17] & [[https://doi.org/10.1002/adma.202003610](https://doi.org/10.1002/adma.202003610)]([https://doi.org/10.1002/adma.202003610](https://doi.org/10.1002/adma.202003610)) & 185 & 37 & 2.99 \\
[18] & [[https://doi.org/10.1021/acs.accounts000785](https://doi.org/10.1021/acs.accounts000785)]([https://doi.org/10.1021/acs.accounts000785](https://doi.org/10.1021/acs.accounts000785)) & 180 & 45 & 4.13 \\
[19] & [[https://doi.org/10.1038/s41570-019-0124-0](https://doi.org/10.1038/s41570-019-0124-0)]([https://doi.org/10.1038/s41570-019-0124-0](https://doi.org/10.1038/s41570-019-0124-0)) & 156 & 26 & 2.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Most impactful documents based on the total citations received. South Korea (381 citations), and Canada (319 citations) are the countries whose authors have received the most citations. However, it should be noted that authors from countries such as Austria and Portugal have contributed a single but impactful document. Hence, the average number of citations received per document should also be considered. Furthermore, considering the international co-authorship rate (14.07%) and the fact that authors from different countries and continents are actively collaborating, the country collaboration network is displayed in Figure 3.  
To obtain a better understanding of the thematic map of the topic, the keywords, both keyword-plus and author keywords, were also examined and are presented in Table 8. Based on the outcomes, the role of AI and its subfields (e.g., machine learning, deep learning, neural networks) in materials science is highlighted. This role becomes more evident when considering the keyword co-occurrence network presented in Figure 4. Specifically, three main clusters arose. Cluster 1 (blue color) refers to machine learning and deep learning and includes terms such as learning algorithms, data mining, forecasting, etc., which are being more widely used in materials science. Cluster 2 (red color) highlights the role of AI and its versatile nature, which enables it to be used for different purposes and in different domains (e.g., drug discovery, simulations, automation, industrial research, etc.). Cluster 3 (green color) highlights the key aspects which the integration of AI in materials science can affect, such as prediction, optimization, design, discovery, generation, etc. Some smaller clusters related to different types of neural networks and