Understanding and optimization of hard magnetic compounds from first principles

Area of Science:

• Computational materials science
• Condensed matter physics
• Magnetism

Background:

• Density functional theory (DFT) is crucial for understanding and designing magnetic materials.
• Accurate modeling of strongly correlated 4f electrons and finite-temperature magnetism requires methodological advancements.
• Rare-earth magnet compounds are vital for technological applications.

Purpose of the Study:

• To review computational schemes for rare-earth magnet compounds.
• To summarize theoretical studies on Nd2Fe14B and RFe12-type magnets.
• To explore data-driven approaches for optimizing multinary magnetic systems.

Main Methods:

• First-principles calculations based on DFT.
• Analysis of chemical substitution and interstitial dopants.
• Bayesian optimization for chemical composition optimization.
• Data-assimilation for predicting finite-temperature magnetization.

Main Results:

• Clarification of the effects of chemical substitution and interstitial dopants on magnetic properties.
• Demonstration of Bayesian optimization for efficient chemical composition tuning.
• Development of a data-assimilation method for predicting magnetization across wide composition spaces.

Conclusions:

• Computational methods, particularly DFT, are powerful for magnetic material design.
• Data-driven approaches significantly enhance the efficiency of materials discovery and property prediction.
• Integration of computational and experimental data is key for advancing finite-temperature magnetism prediction.