IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v589y2022ics037843712100892x.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037843712100892X
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2021.126653?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Thomas J. Hardin & Michael Chandross & Rahul Meena & Spencer Fajardo & Dimitris Giovanis & Ioannis Kevrekidis & Michael L. Falk & Michael D. Shields, 2024. "Revealing the hidden structure of disordered materials by parameterizing their local structural manifold," Nature Communications, Nature, vol. 15(1), pages 1-11, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:589:y:2022:i:c:s037843712100892x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.