Kwon H Y, Kim N J, Lee C K, Yoon H G, Choi J W, Won C
Department of Physics, Kyung Hee University, Seoul, 02447, South Korea.
Center for Spintronics, Korea Institute of Science and Technology, Seoul, 02792, South Korea.
Sci Rep. 2019 Nov 13;9(1):16706. doi: 10.1038/s41598-019-53411-y.
We propose a new efficient algorithm to simulate magnetic structures numerically. It contains a generative model using a complex-valued neural network to generate k-space information. The output information is hermitized and transformed into real-space spin configurations through an inverse fast Fourier transform. The Adam version of stochastic gradient descent is used to minimize the magnetic energy, which is the cost of our algorithm. The algorithm provides the proper ground spin configurations with outstanding performance. In model cases, the algorithm was successfully applied to solve the spin configurations of magnetic chiral structures. The results also showed that a magnetic long-range order could be obtained regardless of the total simulation system size.
我们提出了一种新的高效算法来对磁性结构进行数值模拟。它包含一个生成模型，该模型使用复值神经网络来生成k空间信息。输出信息经过厄米化处理，并通过快速傅里叶逆变换转换为实空间自旋构型。使用随机梯度下降的Adam版本来最小化磁能，磁能是我们算法的代价。该算法以出色的性能提供了合适的基态自旋构型。在模型案例中，该算法成功应用于求解磁性手性结构的自旋构型。结果还表明，无论总模拟系统大小如何，都可以获得磁长程序。
