Multi-scale simplified residual convolutional neural network model for predicting compositions of binary magnesium alloys

• This work proposed a MS-SRCNN model for predicting compositions of as-cast Mg-Nd binary alloys. • The model's prediction accuracy exceeded 98 % (R 2 ). • Compared with classic networks, the MS-SRCNN model reduced runtime by >90 %. • The MS-SRCNN model had excellent generalization capacity in other as-cast Mg-based binary alloys. This study proposes a multi-scale simplified residual convolutional neural network (MS-SRCNN) for the precise prediction of Mg-Nd binary alloy compositions from scanning electron microscope (SEM) images. A multi-scale data structure is established by spatially aligning and stacking SEM images at different magnifications. The MS-SRCNN significantly reduces computational runtime by over 90 % compared to traditional architectures like ResNet50, VGG16, and VGG19, without compromising prediction accuracy. The model demonstrates more excellent predictive performance, achieving a >5 % increase in R 2 compared to single-scale models. Furthermore, the MS-SRCNN exhibits robust composition prediction capability across other Mg-based binary alloys, including Mg-La, Mg-Sn, Mg-Ce, Mg-Sm, Mg-Ag, and Mg-Y, thereby emphasizing its generalization and extrapolation potential. This research establishes a non-destructive, microstructure-informed composition analysis framework, reduces characterization time compared to traditional experiment methods and provides insights into the composition-microstructure relationship in diverse material systems.