Mutual information prediction for strongly correlated systems

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Mutual information prediction for strongly correlated systems
Original language description
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.
Czech name
—
Czech description
—

Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10403 - Physical chemistry

Project
<a href="/en/project/GJ19-13126Y" target="_blank" >GJ19-13126Y: Deep learning for strongly correlated systems in quantum chemistry</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)