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Global exploration of phase behavior in frustrated Ising models using unsupervised learning techniques

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  • Rodrigues de Assis Elias, Danilo
  • Granato, Enzo
  • de Koning, Maurice

Abstract

We apply a set of machine-learning (ML) techniques for the global exploration of the phase diagrams of two frustrated 2D Ising models with competing interactions. Based on raw Monte Carlo spin configurations generated for random system parameters, we apply principal-component analysis (PCA) and auto-encoders to achieve dimensionality reduction, followed by clustering using the DBSCAN method and a support-vector machine classifier to construct the transition lines between the distinct phases in both models. The results are in very good agreement with available exact solutions, with the auto-encoders leading to quantitatively superior estimates, even for a data set containing only 1400 spin configurations. In addition, the results suggest the existence of a relationship between the structure of the optimized auto-encoder latent space and physical characteristics of both systems. This indicates that the employed approach can be useful in perceiving fundamental properties of physical systems in situations where a priori theoretical insight is unavailable.

Suggested Citation

  • Rodrigues de Assis Elias, Danilo & Granato, Enzo & de Koning, Maurice, 2022. "Global exploration of phase behavior in frustrated Ising models using unsupervised learning techniques," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    • Handle: RePEc:eee:phsmap:v:589:y:2022:i:c:s037843712100892x
    DOI: 10.1016/j.physa.2021.126653
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    References listed on IDEAS

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    1. Constantia Alexandrou & Andreas Athenodorou & Charalambos Chrysostomou & Srijit Paul, 2020. "The critical temperature of the 2D-Ising model through deep learning autoencoders," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 93(12), pages 1-15, December.
    2. Rodrigo Freitas & Evan J. Reed, 2020. "Uncovering the effects of interface-induced ordering of liquid on crystal growth using machine learning," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
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    Cited by:

    1. Mokshin, Anatolii V. & Khabibullin, Roman A., 2022. "Is there a one-to-one correspondence between interparticle interactions and physical properties of liquid?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).

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