Multiferroic materials, particularly rare-earth orthochromates (RECrO \(_3\) ), have garnered significant interest due to their unique magnetic and electric-polar properties, making them promising candidates for multifunctional devices. Although extensive research has been conducted on their antiferromagnetic (AFM) transition temperature (N \(\acute{\mathrm{e}}\) el temperature, \(T_\mathrm{N}\) ), ferroelectricity, and piezoelectricity, the effects of doping and substitution of rare-earth (RE) elements on these properties remain insufficiently explored. In this study, convolutional neural networks (CNNs) were employed to predict and analyze the physical properties of RECrO \(_3\) compounds under various doping scenarios. Experimental and literature data were integrated to train machine learning models, enabling accurate predictions of \(T_\mathrm{N}\) , besides remanent polarization ( \(P_\mathrm{r}\) ) and piezoelectric coefficients ( \(d_{33}\) ). The results indicate that doping with specific RE elements significantly impacts \(T_\mathrm{N}\) , with optimal doping levels identified for enhanced performance. Furthermore, high-entropy RECrO \(_3\) compounds were systematically analyzed, demonstrating how the inclusion of multiple RE elements influences magnetic properties. This work establishes a robust framework for predicting and optimizing the properties of RECrO \(_3\) materials, offering valuable insights into their potential applications in energy storage and sensor technologies.
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