Purely single-bonded spiral nitrogen chains stabilized by trivalent lanthanum ions.

Inspired by the single-bonded nitrogen chains stabilized by tetravalent cerium, pentavalent tantalum, and hexavalent tungsten atoms, we explored the possibility of single-bonded nitrogen polymorphs stabilized by trivalent lanthanum ions. To achieve this, we utilized the crystal structure search method on the phase diagram of binary La-N compounds. We identified three novel thermodynamically stable phases, the C2/c LaN3, P-1 LaN4, and P-1 LaN8.

MAGUS: machine learning and graph theory assisted universal structure searcher.

Crystal structure predictions based on first-principles calculations have gained great success in materials science and solid state physics. However, the remaining challenges still limit their applications in systems with a large number of atoms, especially the complexity of conformational space and the cost of local optimizations for big systems. Here, we introduce a crystal structure prediction method, MAGUS, based on the evolutionary algorithm, which addresses the above challenges with machine learning and graph theory.

Prediction of surface reconstructions using MAGUS.

In this paper, we present a new module to predict the potential surface reconstruction configurations of given surface structures in the framework of our machine learning and graph theory assisted universal structure searcher. In addition to random structures generated with specific lattice symmetry, we made full use of bulk materials to obtain a better distribution of population energy, namely, randomly appending atoms to a surface cleaved from bulk structures or moving/removing some of the atoms on the surface, which is inspired by natural surface reconstruction processes. In addition, we borrowed ideas from cluster predictions to spread structures better between different compositions, considering that surface models of different atom numbers usually have some building blocks in common.