Correction: Artificial Intelligence Design of Sustainable Aluminum Alloys: A Review

In the article entitled “Artificial Intelligence Design of Sustainable Aluminum Alloys: A Review” (Computers, Materials & Continua, 2026, Vol. 86, No. 2, pp. 1–33, doi:10.32604/cmc.2025.070735), the following corrections have been made:

1.   The citation for Fig. 5 was incorrect in the published version. The correct source has now been added as Reference [60]. Accordingly, References [60–77] have been renumbered to maintain the correct citation sequence throughout the article.

Figure 5: Application of natural language processing in extraction of aluminum alloy properties from literature. Reprinted with permission from Reference [60]. Copyright 2024, Springer Nature.

New Reference [60]: Montanelli L, Venugopal V, Olivetti EA, Latypov MI. High-throughput extraction of phase–property relationships from literature using natural language processing and large language models. Integr Mater Manuf Innov. 2024;13;396–405. https://doi.org/10.1007/s40192-024-00344-8.

2.   The copyright years for Figs. 10 and 11 were incorrectly given as 2022. They have now been corrected to 2021.

Figure 10: (a,c) and (d,e) Experimental vs. predicted hardness of various models of the testing data set and the related R2 and MSE recorded for the employed algorithms without optimization and with optimization, respectively, (b) Hardness distribution profile for the input dataset. Reprinted with permission from Reference [35]. Copyright 2021, Elsevier.

Figure 11: The loop used for designing high performance Al-Cu-Mg alloys based on the machine learning technique. Reprinted with permission from Reference [35]. Copyright 2021, Elsevier.

The authors confirm that these corrections do not affect the results, discussion, or scientific conclusions of the article. The authors apologize for any inconvenience caused.

This correction was approved by the Computers, Materials & Continua Editorial Office. The original publication has been updated accordingly.