Search

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. [ABSTRACT FROM AUTHOR]

Computational materials discovery is a booming field of science, which helps in predicting new unexpected materials with optimal combinations of various physical properties. Going beyond the targeted search for new materials within prespecified systems, the recently developed method, Mendelevian search, allows one to look for materials with the desired properties across the entire Periodic Table, indicating possibly superhard (or other) materials that could be obtained experimentally. From this viewpoint, we discuss the recently developed methods for crystal structure prediction and empirical models of Vickers hardness and fracture toughness that allow fast screening for materials with optimal mechanical properties. We also discuss the results of the computational search for hard and superhard materials obtained in the last few years using these novel approaches and present a "treasure map" of hard and superhard materials, which summarizes known and predicted materials and points to promising future directions of superhard materials discovery. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this study, we perform a systematic search to find the possible lowest energy structure of silicon nanoclusters Si n ( 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.