Mutual information prediction for strongly correlated systems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61388955%3A_____%2F23%3A00566845" target="_blank" >RIV/61388955:_____/23:00566845 - isvavai.cz</a>

Výsledek na webu

<a href="https://hdl.handle.net/11104/0338119" target="_blank" >https://hdl.handle.net/11104/0338119</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.cplett.2023.140297" target="_blank" >10.1016/j.cplett.2023.140297</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Mutual information prediction for strongly correlated systems

Popis výsledku v původním jazyce

We have trained a new machine-learning (ML) model which predicts mutual information (MI) for strongly correlated systems. This is a complex quantity, which is much more difficult to predict than one-site entropies, but carries important information about the correlation structure inside electronic systems. In this work, we replaced the expensive density matrix renormalization group (DMRG) calculations by newly trained ML model for prediction of the mutual information. We show the performance of the model on two important tasks: (a) to determine the correlation structure and (b) to determine ordering of orbitals for accurate DMRG calculations. The results are compared with the MI obtained from accurate DMRG calculations.

Název v anglickém jazyce

Mutual information prediction for strongly correlated systems

Popis výsledku anglicky

We have trained a new machine-learning (ML) model which predicts mutual information (MI) for strongly correlated systems. This is a complex quantity, which is much more difficult to predict than one-site entropies, but carries important information about the correlation structure inside electronic systems. In this work, we replaced the expensive density matrix renormalization group (DMRG) calculations by newly trained ML model for prediction of the mutual information. We show the performance of the model on two important tasks: (a) to determine the correlation structure and (b) to determine ordering of orbitals for accurate DMRG calculations. The results are compared with the MI obtained from accurate DMRG calculations.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10403 - Physical chemistry

Projekt

<a href="/cs/project/GJ19-13126Y" target="_blank" >GJ19-13126Y: Deep learning pro silně korelované systémy v kvantové chemii</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Chemical Physics Letters

ISSN

0009-2614

e-ISSN

1873-4448

Svazek periodika

813

Číslo periodika v rámci svazku

FEB 2023

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

7

Strana od-do

140297

Kód UT WoS článku

001035794700001

EID výsledku v databázi Scopus

2-s2.0-85145854789