Genetic-tunneling driven energy optimizer for magnetic system
- URL: http://arxiv.org/abs/2301.00207v1
- Date: Sat, 31 Dec 2022 14:27:34 GMT
- Title: Genetic-tunneling driven energy optimizer for magnetic system
- Authors: Qichen Xu, Zhuanglin Shen, Manuel Pereiro, Pawel Herman, Olle Eriksson
and Anna Delin
- Abstract summary: We propose a genetic-tunneling-driven variance-controlled optimization approach, which combines a local energy minimizer back-end and a metaheuristic global searching front-end.
We demonstrate here the success of this method in searching for magnetic ground states of 2D monolayer systems with both artificial and calculated interactions from density functional theory.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Novel topological spin textures, such as magnetic skyrmions, benefit from
their inherent stability, acting as the ground state in several magnetic
systems. In the current study of atomic monolayer magnetic materials,
reasonable initial guesses are still needed to search for those magnetic
patterns. This situation underlines the need to develop a more effective way to
identify the ground states. To solve this problem, in this work, we propose a
genetic-tunneling-driven variance-controlled optimization approach, which
combines a local energy minimizer back-end and a metaheuristic global searching
front-end. This algorithm is an effective optimization solution for searching
for magnetic ground states at extremely low temperatures and is also robust for
finding low-energy degenerated states at finite temperatures. We demonstrate
here the success of this method in searching for magnetic ground states of 2D
monolayer systems with both artificial and calculated interactions from density
functional theory. It is also worth noting that the inherent concurrent
property of this algorithm can significantly decrease the execution time. In
conclusion, our proposed method builds a useful tool for low-dimensional
magnetic system energy optimization.
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