Work

Machine learning vortices at the Kosterlitz-Thouless transition

Year: 2018

Type: article

Abstract: The authors extend the recent successful application of neural networks fordistinguishing phases of matter to the case of topologically driven phase transitions. They demonstrate that supervised networks correctly identify the Kosterlitz-Thouless transition in the two-dimensional classical XY model. However, their analysis suggests that a naive classification of phases is based on bulk features, rather than on detecting local topological defects. While they show that it is possible to fine-tune a network to recognize vortices, it generally remains a difficult task to detect topological defects without $a\phantom{\rule{0.333em}{0ex}}p\phantom{\rule{0}{0ex}}r\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}o\phantom{\rule{0}{0ex}}r\phantom{\rule{0}{0ex}}i$ knowledge.

Source: Physical review. B./Physical review. B

DOI: 10.1103/physrevb.97.045207

Authors: Matthew J. S. Beach, Anna Golubeva, Roger G. Melko

Institutions: University of Waterloo, Perimeter Institute

Language: English

Topic: Theoretical and Computational Physics

Subfield: Condensed Matter Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green