相关论文: Accelerated discovery and design of Fe-Co-Zr magne…

Cerium-based intermetallics have garnered significant research attention as potential new permanent magnets. In this study, we explore the compositional and structural landscape of Ce-Co-Cu ternary compounds using a machine learning…

In this work we perform a systematic calculation of the Fe-Ta phase diagram to discover novel hard magnetic phases. By using structure prediction methods based on evolutionary algorithms, we identify two new energetically stable magnetic…

The binary manganese aluminium (MnAl) alloy with L$1_0$ crystal structure is a promising rare earth element-free permanent magnetic material because of its exceptional magnetic properties. However, experimentally synthesizing it in a stable…

The discovery of novel quantum materials within ternary phase spaces containing antagonistic pair such as Fe with Bi, Pb, In, and Ag, presents significant challenges yet holds great potential. In this work, we investigate the stabilization…

Machine Learning (ML) plays an increasingly important role in the discovery and design of new materials. In this paper, we demonstrate the potential of ML for materials research using hard-magnetic phases as an illustrative case. We build…

By understanding the small easy axis magnetocrystalline anisotropy energy (MAE) of hexagonal Ce2Co17, an attempt has been made to improve anisotropy and consequently to obtain better characteristics for a high energy permanent magnet via…

An integrated data-driven approach combined with a high-throughput framework based on first-principles calculations was used to discover novel rare-earth-free permanent magnets, focusing on binary alloys. Compounds were screened…

A new class of rare-earth-free permanent magnets is proposed. The parent compound of this class is Co$_3$Mn$_2$Ge, and its discovery is the result of first principles theory combined with experimental synthesis and characterisation. The…

We employ a combination of machine learning and first-principles calculations to predict magnetic properties of rare-earth lean magnets. For this purpose, based on training set constructed out of experimental data, the machine is trained to…

Combining material informatics and high-throughput electronic structure calculations offers the possibility of a rapid characterization of complex magnetic materials. Here we demonstrate that datasets of electronic properties calculated at…

Structures and magnetic properties of Fe16-xCoxN2 are studied using adaptive genetic algorithm and first-principles calculations. We show that substituting Fe by Co in Fe16N2 with Co/Fe ratio smaller than 1 can greatly improve the magnetic…

We report the discovery of a new allotrope of iron by first principles calculations. This phase has $Pmn2_1$ symmetry, a six-atom unit cell (hence the name Fe$_6$), and the highest magnetization density (M$_s$) among all known crystalline…

We theoretically investigate magnetic anisotropy in materials with non-critical elements to determine which symmetry conditions and atomic shell filling favor enhanced magnetic anisotropy. We study the magnetocrystalline anisotropies (MCA)…

Rare earth free alloys are in focus of permanent magnet research since the accessibility of the elements needed for nowadays conventional magnets is limited. Tetragonally strained iron-cobalt (Fe-Co) has attracted large interest as…

Multiscale simulation is a key research tool for the quest for new permanent magnets. Starting with first principles methods, a sequence of simulation methods can be applied to calculate the maximum possible coercive field and expected…

In rare-earth permanent magnets (REPM's), trade-off's between intrinsic magnetic properties are often encountered. A recent example is SmFe$_{12}$ where excellent magnetic properties can be achieved at the sacrifice of bulk structure…

The pursuit of high-performance, rare-earth-free magnetostrictive materials is crucial for advancing technologies in sensing, actuation, and microelectromechanical systems. Heusler alloys represent a promising, yet underexplored, class of…

The datasets presented in this article are related to materials research on hard ferromagnet in TM-Fe-Si (TM=Ti, Zr, Hf, V, Nb, and Ta) ternary systems. The motivation for data collection is based on the research paper entitled "Novel hard…

We develop a machine-learning method for coarse-graining condensed-phase molecular systems using anisotropic particles. The method extends currently available high-dimensional neural network potentials by addressing molecular anisotropy. We…

We present a computational study of the compound Y(Co$_{1-x-y}$Fe$_x$Cu$_y$)$_5$ for 0 $\leq x,y \leq 0.2$. This compound was chosen as a prototype for investigating the cell boundary phase believed to play a key role in establishing the…