Quantum Chemical Density Matrix Renormalization Group Method Boosted by Machine Learning
Source abstract
Abstract
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.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Quantum many-body systems · Machine Learning in Materials Science · Spectroscopy and Quantum Chemical Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
J. Heyrovsky Institute of Physical Chemistry, v.v.i.
Czech Academy of Sciences
Applied Mathematics and Computational Research Division
Lawerence Berkeley National Laboratory
Department of Chemistry and Biochemistry
University of California, Santa Barbara
Department of Materials
J. Heyrovský Institute of Physical Chemistry, v.v.i.
Funding
European Commission · Basic Energy Sciences · Ministerstvo ?kolstv?, Ml?de?e a Telov?chovy · Grantov? Agentura Cesk? Republiky
References (39)
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