Neural Networks for Identifying Phase Transitions
DOI:
https://doi.org/10.14295/vetor.v35i1.18372Keywords:
Phase Transitions, Machine Learning, Ising ModelAbstract
In the present work, an introduction was provided to the use of machine learning techniques for identifying the phases of a magnetic system and the transition between these phases. For this, the two-dimensional Ising Model was used, and a single-layer Perceptron network was implemented. The results obtained indicated good accuracy in detecting the phase transition temperature in this model, confirming the feasibility of employing these techniques in the study of phase transitions.Downloads
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