Walker Nicholas, Tam Ka-Ming, Jarrell Mark
Department of Physics and Astronomy, Louisiana State University, Baton Rouge, LA, 70803, USA.
Center for Computation and Technology, Louisiana State University, Baton Rouge, LA, 70803, USA.
Sci Rep. 2020 Aug 3;10(1):13047. doi: 10.1038/s41598-020-69848-5.
The 2-dimensional Ising model on a square lattice is investigated with a variational autoencoder in the non-vanishing field case for the purpose of extracting the crossover region between the ferromagnetic and paramagnetic phases. The encoded latent variable space is found to provide suitable metrics for tracking the order and disorder in the Ising configurations that extends to the extraction of a crossover region in a way that is consistent with expectations. The extracted results achieve an exceptional prediction for the critical point as well as agreement with previously published results on the configurational magnetizations of the model. The performance of this method provides encouragement for the use of machine learning to extract meaningful structural information from complex physical systems where little a priori data is available.
为了提取铁磁相和顺磁相之间的交叉区域，我们使用变分自编码器研究了非零场情况下正方形晶格上的二维伊辛模型。结果发现，编码后的潜在变量空间为跟踪伊辛构型中的有序和无序提供了合适的度量，这种度量以与预期一致的方式扩展到交叉区域的提取。提取结果对临界点实现了出色的预测，并且与之前发表的关于该模型构型磁化强度的结果一致。该方法的性能为利用机器学习从几乎没有先验数据的复杂物理系统中提取有意义的结构信息提供了鼓励。
