Year: 2017
Type: article
Abstract: Machine learning has strong potential as a tool for understanding how to classify phases in condensed matter physics. A new investigation shows that an artificial neural network can be trained to identify changes in the collective magnetic properties of electrons on a lattice and predict trends in the transition when some of the electrons are removed.
Source: Physical Review X
Authors: Kelvin Ch’ng, Juan Carrasquilla, Roger G. Melko, Ehsan Khatami
Language: English
Subfield: Condensed Matter Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $4,200
Funders: National Science Foundation, Innovation, Science and Economic Development Canada, Government of Canada, Canada Excellence Research Chairs, Government of Canada, Institut Périmètre de physique théorique, Natural Sciences and Engineering Research Council of Canada, Division of Materials Research
OpenAlex ID: W2520014252