MagGene: A genetic evolution program for magnetic structure prediction

Abstract We have developed a software MagGene to predict magnetic structures by using genetic algorithm. Starting from an atom structure, MagGene repeatedly generates new magnetic structures and calls first-principles calculation engine to get the most stable magnetic structure. This software is applicable to both collinear and noncollinear systems. It is particularly convenient for predicting the magnetic structures of atomic systems with strong spin–orbit couplings and/or strong spin frustrations. Program summary Program Title: MagGene CPC Library link to program files: http://dx.doi.org/10.17632/m83gcp5z48.1 Licensing provisions: MIT Programming language: Fortran 90 Nature of problem: In complex magnetic systems, such as systems with strong spin–orbit couplings and/or strong spin-frustrations, the associated magnetic structures could also be quite complex. The traditional methods, such as theoretical analysis based on crystal symmetries and simulated annealing based on a special model Hamiltonian, are inefficient. Then the electronic structures, spin wave dispersions, and other properties based on correct magnetic structures could not be obtained accurately. Therefore, an efficient method to predict magnetic structures is required. Solution method: The magnetic structures can be predicted efficiently by using genetic algorithm. Additional comments including unusual features: The magnetic structures can be predicted for both collinear and noncollinear atomic systems by using genetic evolution algorithm. It is flexible to use in variety of systems. The total magnetic moment can be fixed.

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