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

TL;DR

This paper uses machine learning, specifically CNNs, to predict how rare-earth doping affects the magnetic and electric properties of orthochromates, aiding in the design of multifunctional materials.

Contribution

It introduces a machine learning framework combining experimental and literature data to predict properties of doped RECrO$_3$ compounds, including high-entropy variants.

Findings

Doping with specific RE elements significantly alters the Néel temperature.

Optimal doping levels enhance magnetic and electric properties.

High-entropy RECrO$_3$ compounds show tunable magnetic behavior.

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