Computational prediction of new magnetic materials.

The discovery of new magnetic materials is a big challenge in the field of modern materials science. We report the development of a new extension of the evolutionary algorithm USPEX, enabling the search for half-metals (materials that are metallic only in one spin channel) and hard magnetic materials. First, we enabled the simultaneous optimization of stoichiometries, crystal structures, and magnetic structures of stable phases. Second, we developed a new fitness function for half-metallic materials that can be used for predicting half-metals through an evolutionary algorithm. We used this extended technique to predict new, potentially hard magnets and rediscover known half-metals. In total, we report five promising hard magnets with high energy product (|BH|MAX), anisotropy field (Ha), and magnetic hardness (κ) and a few half-metal phases in the Cr-O system. A comparison of our predictions with experimental results, including the synthesis of a newly predicted antiferromagnetic material (WMnB2), shows the robustness of our technique.

New candidates for the global minimum of medium-sized silicon clusters: A hybrid DFTB/DFT genetic algorithm applied to Si n , n = 8-80.

In this study, we perform a systematic search to find the possible lowest energy structure of silicon nanoclusters Sin (n = 8-80) by means of an evolutionary algorithm. The fitness function for this search is the total energy of density functional tight binding (DFTB). To be on firm ground, we take several low energy structures of DFTB and perform further geometrical optimization by density functional theory (DFT). Then we choose structures with the lowest DFT total energy and compare them with the reported lowest energy structures in the literature. In our search, we found several lowest energy structures that were previously unreported. We further observe a geometrical transition at n = 27 from elongated to globular structures. In addition, the optical gap of the lowest energy structures is investigated by time-dependent DFTB (TD-DFTB) and time-dependent DFT (TD-DFT). The results show the same trend in TD-DFTB and TD-DFT for the optical gap. We also find a sudden drop in the optical gap at n = 27, precisely where the geometrical transition occurs.

Computational Search for Novel Hard Chromium-Based Materials.

Nitrides, carbides, and borides of transition metals are an attractive class of hard materials. Our recent preliminary explorations of the binary chemical compounds indicated that chromium-based materials are among the hardest transition metal compounds. Motivated by this, here we explore in detail the binary Cr-B, Cr-C, and Cr-N systems using global optimization techniques. Calculated enthalpy of formation and hardness of predicted materials were used for Pareto optimization to define the hardest materials with the lowest energy. Our calculations recover all numerous known stable compounds (except Cr23C6 with its large unit cell) and discover a novel stable phase Pmn21-Cr2C. We resolve the structure of Cr2N and find it to be of anti-CaCl2 type (space group Pnnm). Many of these phases possess remarkable hardness, but only CrB4 is superhard (Vickers hardness 48 GPa). Among chromium compounds, borides generally possess the highest hardnesses and greatest stability. Under pressure, we predict stabilization of a layered TMDC-like phase of Cr2N, a WC-type phase of CrN, and a new compound CrN4. Nitrogen-rich chromium nitride CrN4 is a high-energy-density material featuring polymeric nitrogen chains. In the presence of metal atoms (e.g., Cr), polymerization of nitrogen takes place at much lower pressures; CrN4 becomes stable at ∼15 GPa (cf. 110 GPa for synthesis of pure polymeric nitrogen).