Machine learning wave functions to identify fractal phases

Autor:	Cadez, Tilen, Dietz, Barbara, Rosa, Dario, Andreanov, Alexei, Slevin, Keith, Ohtsuki, Tomi
Rok vydání:	2023
Předmět:	Condensed Matter - Disordered Systems and Neural Networks Condensed Matter - Mesoscale and Nanoscale Physics
Druh dokumentu:	Working Paper
Popis:	We demonstrate that an image recognition algorithm based on a convolutional neural network provides a powerful procedure to differentiate between ergodic, non-ergodic extended (fractal) and localized phases in various systems: single-particle models, including random-matrix and random-graph models, and many-body quantum systems. The network can be successfully trained on a small data set of only 500 wave functions (images) per class for a single model. The trained network can then be used to classify phases in the other models and is thus very efficient. We discuss the strengths and limitations of the approach. Comment: 9 pages, 10 figures + 2 pages, 4 figures, 1 table in appendices
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2306.01402 Zobrazit plný text záznamu View this record from Arxiv