Synergistic effects of rare-earth doping on the magnetic properties of orthochromates: A machine learning approach

Abstract

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$\acute{\text{e}}$el temperature, $T_{\text{N}}$), 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 $T_{\text{N}}$, besides remanent polarization ($P_{\text{r}}$) and piezoelectric coefficients ($d_{33}$). The results indicate that doping with specific RE elements significantly impacts $T_{\text{N}}$, 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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