Ab initioscreening methodology applied to the search for new permanent magnetic materials

Abstract

Machine learning (ML) is increasingly becoming a helpful tool in the search for novel functional compounds. Here we use classification via random forests to predict the stability of half-Heusler (HH) compounds, using only experimentally reported compounds as a training set. Cross-validation yields an excellent agreement between the fraction of compounds classified as stable and the actual fraction of truly stable compounds in the ICSD. The ML model is then employed to screen 71,178 different 1:1:1 compositions, yielding 481 likely stable candidates. The predicted stability of HH compounds from three previous high throughput ab initio studies is critically analyzed from the perspective of the alternative ML approach. The incomplete consistency among the three separate ab initio studies and between them and the ML predictions suggests that additional factors beyond those considered by ab initio phase stability calculations might be determinant to the stability of the compounds. Such factors can include configurational entropies and quasihar-monic contributions.

Magnetic properties and magnetocrystalline anisotropy of Nd2Fe17, Nd2Fe17X3, and related compounds

“…Similarity Drebov et al . 20 used open-core method for treatment of f -electrons by assuming RE 3+ configuration. Such treatment of f electrons cannot reproduce some of the observed magnetic properties.…”

“…One possibility for changing the easy axis direction is through alloying. Previous theoretical calculations 20,21 for these compounds treat Nd f electrons as core electrons (also known as open core approximation) which do not take part in hybridization. However for the rare earth elements the most of the contribution to MAE is shown to be from f electrons, hence a proper treatment of f electrons is crucial for accurate description of MAE 22–24 .…”

Novel Permanent Magnets by High-Throughput Experiments

“…The methods applied are the highly accurate but slow density functional theory (DFT) or the less accurate but faster tight-binding-linear muffin-tin-orbitalatomic-sphere-approximation methods. HTS is deployed in searching for novel RE-containing and RE-free hard magnetic compounds 15,16 as well as, e.g., Heusler compounds. [17][18][19][20][21] On the experimental side, the most prominent high-throughput methods are the film libraries 22,23 and bulk 5,24 approaches to scan higher component systems for novel compounds within a reasonable time frame.…”

Magnetic properties of hard magnetic (Fe,Cr)3Sn2 intermetallic compound

“…Novel magnet materials filling the wide‐open gap between cost‐efficient hard ferrites and expensive high‐performance Fe–Nd–B are essential for efficient energy converters. Recently, computational ab‐initio and experimental thin film and bulk high‐throughput approaches have been developed to screen so far unexplored ternary and higher component systems for such materials. Although since then several promising novel hard magnetic phases have been discovered like Fe 11– x Co x TiCe , Fe 11 WCe , Fe 11 SiCe and Fe 12 NdN x , none of them is free of rare‐earth (RE) met al According to Sales et al binary Fe 3 Sn 2 may have potential as hard magnetic material by alloying X resulting in ternary (Fe,X) 3 Sn 2 .…”