Symbolic Regression‐Guided Feature Engineering for Predicting Magnetization in Cu‐Based Alloys Under Data‐Scarce Conditions

ABSTRACT By integrating physics‐informed feature engineering with symbolic regression (specifically, the sure independence screening and sparsifying operator or SISSO), the magnetic properties of Cu‐based alloys have been predicted based on a small experimental dataset ( n = 76). A set of atomic descriptors was constructed, achieving predictive performance for saturation magnetization ( M s ) and remanent magnetization ( M r ). From in‐training set fitting, the coefficients of determination ( R 2 ) of M s and M r are 0.981 and 0.936, respectively. Further leave‐one‐out cross‐validation (LOOCV) on the alloy dataset excluding three pure metal samples demonstrated robust prediction maintaining a cross‐validated R 2 cv of 0.945 for M s . The mathematical expressions generated by SISSO reveal the synergistic effects of processing parameters and atomic properties on magnetic performance. Experimental validation on two newly designed alloy compositions confirmed the generalization ability with relative errors for M s predictions below 12.8%. Moreover, a cross‐system validation on Fe‐based medium manganese steel captured M s of the sample annealed at relatively low temperature (973 K) with low relative error (11.2%), suggesting the model to be transferable to other alloy systems. On the other hand, the model's limited ability to predict coercivity ( H c ) and the properties of these samples that have undergone phase transformations highlight the necessity of incorporating microstructural descriptors in future.