Unsupervised machine learning phase classification for the Falicov-Kimball model

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F25%3A00635853" target="_blank" >RIV/68378271:_____/25:00635853 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11320/25:10498244

Výsledek na webu

<a href="https://doi.org/10.1103/PhysRevB.111.205116" target="_blank" >https://doi.org/10.1103/PhysRevB.111.205116</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1103/PhysRevB.111.205116" target="_blank" >10.1103/PhysRevB.111.205116</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Unsupervised machine learning phase classification for the Falicov-Kimball model

Popis výsledku v původním jazyce

We apply various unsupervised machine learning methods for phase classification to investigate the finite-temperature phase diagram of the spinless Falicov-Kimball model in two dimensions. Using only particle occupation snapshots from Monte Carlo simulations as input, each technique, including a straightforward classification based on principal component analysis (PCA), successfully identifies the phase boundary between ordered and disordered phases, independent of the type of phase transition. Remarkably, these techniques also distinguish between the weakly localized and Anderson-localized regimes within the disordered phase, accurately identifying their crossover, which is a challenging task for standard methods. Among the machine learning approaches used, PCA based analysis outperforms more complex methods, such as neural network predictors and autoencoders. These results underscore the effectiveness of simple unsupervised techniques in examining phase transitions and electron localization in complex correlated systems.

Název v anglickém jazyce

Unsupervised machine learning phase classification for the Falicov-Kimball model

Popis výsledku anglicky

We apply various unsupervised machine learning methods for phase classification to investigate the finite-temperature phase diagram of the spinless Falicov-Kimball model in two dimensions. Using only particle occupation snapshots from Monte Carlo simulations as input, each technique, including a straightforward classification based on principal component analysis (PCA), successfully identifies the phase boundary between ordered and disordered phases, independent of the type of phase transition. Remarkably, these techniques also distinguish between the weakly localized and Anderson-localized regimes within the disordered phase, accurately identifying their crossover, which is a challenging task for standard methods. Among the machine learning approaches used, PCA based analysis outperforms more complex methods, such as neural network predictors and autoencoders. These results underscore the effectiveness of simple unsupervised techniques in examining phase transitions and electron localization in complex correlated systems.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10302 - Condensed matter physics (including formerly solid state physics, supercond.)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Physical Review B

ISSN

2469-9950

e-ISSN

2469-9969

Svazek periodika

111

Číslo periodika v rámci svazku

20

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

16

Strana od-do

205116

Kód UT WoS článku

001494685000001

EID výsledku v databázi Scopus

2-s2.0-105005146138