Quantum Chemical Density Matrix Renormalization Group Method Boosted by Machine Learning
The use of machine learning (ML) to refine low-level theoretical calculations to achieve higher accuracy is a promising and actively evolving approach known as Δ-ML. The density matrix renormalization group (DMRG) is a powerful variational approach widely used for studying strongly correlated quantum systems. High computational efficiency can be achieved without compromising accuracy. Here, we demonstrate the potential of a simple ML model to significantly enhance the performance of the quantum chemical DMRG method.
Authors
- Pavlo Golub
- Libor Veis (ORCID: https://orcid.org/0000-0002-4229-6335)
- Vojtěch Vlček (ORCID: https://orcid.org/0000-0002-2836-7619)
- Chao Yang (ORCID: https://orcid.org/0000-0001-7172-7539)
Institutions
- Lawrence Berkeley National Laboratory (US)
- University of California, Santa Barbara (US)
- Czech Academy of Sciences (CZ)
- Czech Academy of Sciences, J. Heyrovský Institute of Physical Chemistry (CZ)
- Applied Mathematics (United States) (US)
Publication Details
- Journal
- The Journal of Physical Chemistry Letters
- Published
- 2025-03-24
- DOI
- https://doi.org/10.1021/acs.jpclett.5c00207
- Primary Topic
- Quantum many-body systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- European Commission
- Ministerstvo Školství, Mládeže a Tělovýchovy
- Grantová Agentura České Republiky
- Basic Energy Sciences