Crystal structure prediction of magnetic materials

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 (H_a), 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.

A polymeric nitrogen N[Formula: see text]-N[Formula: see text] system with enhanced stability at low pressure

Postulated in 1992 and synthesized in 2004 above 2000 K and 110 GPa, the singly-bonded nitrogen cubic gauche crystal (cg-PN) is still considered to be the ultimate high energy density material (HEDM). The search however has continued for a method to synthesize cg-PN at more ambient conditions or find HEDMs which can be synthesized at lower pressure and temperature. Here, using ab initio evolutionary crystal prediction techniques, a simpler nitrogen-based molecular crystal consisting of N[Formula: see text] and N[Formula: see text] molecules is revealed to be a more favorable polynitrogen at lower pressures. The energetic gain of  534 meV/atom over cg-PN and  138 meV/atom over the N[Formula: see text] molecular crystal at zero pressure makes the N[Formula: see text]-N[Formula: see text] system more appealing. Dynamical and mechanical stabilities are investigated at 5 and 0 GPa, and vibrational frequencies are assessed for its Raman and IR spectra. The prospects of an experimental synthesis of the N[Formula: see text]-N[Formula: see text] polymeric system compared to cg-PN is higher because the C[Formula: see text] symmetry of N[Formula: see text] within this crystal would be easier to target from the readily available N[Formula: see text] azides and the observed N[Formula: see text] and N[Formula: see text] radicals.

Predicting the crystal structure of [Formula: see text] high energy density material using ab initio evolutionary algorithms

[Formula: see text] is the first successfully synthesized salt that has a polymeric nitrogen moeity ([Formula: see text]). Although 12 other [Formula: see text] salts followed, with [Formula: see text] and [Formula: see text] being the most stable, the crystal structure of [Formula: see text] remains unknown. Currently, it is impossible to experimentally determine the structures of [Formula: see text] due to its marginal stability and explosive nature. Here, following an ab initio evolutionary prediction and using only the stoichiometry of [Formula: see text] as a starting point, we were able to reveal the crystal structure of this high energy density material (HEDM). The [Formula: see text] symmetry of the [Formula: see text] cation, as suggested from earlier investigations, is confirmed to be the symmetry adopted by this polymeric nitrogen within the crystal. This result gave full confidence in the validity of this crystal prediction approach. While stability of the [Formula: see text] within the crystal is found to be driven by electronic considerations, the marginal stability of this HEDM is found to be related to a partial softening of its phonon modes.

Machine-enabled inverse design of inorganic solid materials: promises and challenges

Developing high-performance advanced materials requires a deeper insight and search into the chemical space. Until recently, exploration of materials space using chemical intuitions built upon existing materials has been the general strategy, but this direct design approach is often time and resource consuming and poses a significant bottleneck to solve the materials challenges of future sustainability in a timely manner. To accelerate this conventional design process, inverse design, which outputs materials with pre-defined target properties, has emerged as a significant materials informatics platform in recent years by leveraging hidden knowledge obtained from materials data. Here, we summarize the latest progress in machine-enabled inverse materials design categorized into three strategies: high-throughput virtual screening, global optimization, and generative models. We analyze challenges for each approach and discuss gaps to be bridged for further accelerated and rational data-driven materials design.