Understanding and optimization of hard magnetic compounds from first principles

Abstract

First-principles calculation based on density functional theory is a powerful tool for understanding and designing magnetic materials. It enables us to quantitatively describe magnetic properties and structural stability, although further methodological developments for the treatment of strongly-correlated 4f electrons and finite-temperature magnetism are needed. Here, we review recent developments of computational schemes for rare-earth magnet compounds, and summarize our theoretical studies on Nd 2 Fe 14 B and R Fe 12 -type compounds. Effects of chemical substitution and interstitial dopants are clarified. We also discuss how data-driven approaches are used for studying multinary systems. Chemical composition can be optimized with fewer trials by the Bayesian optimization. We also present a data-assimilation method for predicting finitetemperature magnetization in wide composition space by integrating computational and experimental data.

Physics-informed machine learning combining experiment and simulation for the design of neodymium-iron-boron permanent magnets with reduced critical-elements content

“…Micromagnetic simulations were used to create the training data. Miyake and Harashima presented a data-assimilation method used to predict finite-temperature magnetization M s and Curie temperature T c for (Nd,Pr,La,Ce) 2 (Fe,Co,Ni) 14 B compositions merging computational and experimental data (Miyake et al, 2021;Harashima et al, 2021). 97.176.35/magnetpredictor, accessed 24-10-2022. Frontiers in Materials 02 frontiersin.org…”

Accelerated discovery of cost-effective Nd–Fe–B magnets through adaptive learning

“…Machine learning (ML) has been widely utilized in a wide range of materials science applications to build predictive models for guiding and accelerating materials design. [43][44][45][46] However, robust models capable of making accurate and reliable predictions are usually built upon a large amount of data, which is not always available. In recent years, adaptive-learning framework has shown great promise in the above scenario, where data are scarce and expensive to come by, in terms of both time and cost.…”