Deep learning on the 2-dimensional Ising model to extract the crossover region with a variational autoencoder
Deep learning on the 2-dimensional Ising model to extract the crossover region with a variational autoencoder
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DOI：
10.1038/s41598-020-69848-5
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发表时间：
2020-05
影响因子：
4.6
通讯作者：
Nicholas Walker;Ka-Ming Tam;M. Jarrell
中科院分区：
文献类型：
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作者：
Nicholas Walker;Ka-Ming Tam;M. Jarrell
在非零场情况下，用变分自动编码器研究了正方形晶格上的二维伊辛模型，以提取铁磁相和顺磁相之间的交叉区。发现编码的潜在变量空间提供了用于跟踪伊辛配置中的有序和无序的适当度量，其扩展到以与预期一致的方式提取交叉区域。提取的结果实现了对临界点的异常预测，并与以前发表的关于该模型构型磁化的结果相一致。这种方法的性能为使用机器学习从先验数据很少的复杂物理系统中提取有意义的结构信息提供了鼓励。
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.