Mutual information prediction for strongly correlated systems

Golub, Pavlo; Antalik, Andrej; Beran, Pavel; Brabec, Jiri

doi:10.1016/j.cplett.2023.140297

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Mutual information prediction for strongly correlated systems

Golub, Pavlo·Antalik, Andrej·Beran, Pavel·Brabec, Jiri

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Abstract

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. In conclusion, the results are compared with the MI obtained from accurate DMRG calculations.

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY Chemistry DMRG Machine learning Mutual information Physics Quantum chemistry Strong correlation

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Golub, Pavlo, Antalik, Andrej, Beran, Pavel, Brabec, Jiri. 2023-01-06. Mutual information prediction for strongly correlated systems. https://doi.org/10.1016/j.cplett.2023.140297

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