Mutual information prediction for strongly correlated systems

0566845 - ÚFCH JH 2024 RIV NL eng J - Journal Article
Golub, Pavlo - Antalík, Andrej - Beran, Pavel - Brabec, Jiří
Mutual information prediction for strongly correlated systems.
Chemical Physics Letters. Roč. 813, FEB 2023 (2023), č. článku 140297. ISSN 0009-2614. E-ISSN 1873-4448
R&D Projects: GA ČR(CZ) GJ19-13126Y
Institutional support: RVO:61388955
Keywords : DMRG * Quantum chemistry * Mutual information * Strong correlation * Machine learning
OECD category: Physical chemistry
Impact factor: 2.8, year: 2022
Method of publishing: Limited access

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.
Permanent Link: https://hdl.handle.net/11104/0338119