# Machine learning for percolation utilizing auxiliary Ising variables.

@article{Zhang2022MachineLF, title={Machine learning for percolation utilizing auxiliary Ising variables.}, author={Junyi Zhang and Boqiang Zhang and Junyi Xu and Wanzhou Zhang and Youjin Deng}, journal={Physical review. E}, year={2022}, volume={105 2-1}, pages={ 024144 } }

Machine learning for phase transition has received intensive research interest in recent years. However, its application in percolation still remains challenging. We propose an auxiliary Ising mapping method for the machine learning study of the standard percolation as well as a variety of statistical mechanical systems in correlated percolation representation. We demonstrate that unsupervised machine learning is able to accurately locate the percolation threshold, independent of the spatial…

## 2 Citations

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We use deep-learning strategies to study the 2D percolation model on a square lattice. We employ standard image recognition tools with a multi-layered convolutional neural network. We test how well…

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- Materials ScienceArXiv
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Machine learning for locating phase diagram has received intensive research interest in recent years. However, its application in automatically locating phase diagram is limited to single closed…

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