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DOE OSTI · 2538716

Quantum Chemical Density Matrix Renormalization Group Method Boosted by Machine Learning

Golub, Pavlo [Czech Academy of Sciences, Prague (Czech Republic)] (ORCID:0000000308344281)·Yang, Chao [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)] (ORCID:0000000171727539)·Vlček, Vojtěch [University of California, Santa Barbara, CA (United States)] (ORCID:0000000228367619)·Veis, Libor [Czech Academy of Sciences, Prague (Czech Republic)] (ORCID:0000000242296335)
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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.

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRYAromatic compoundsChemical calculationsEnergyHydrocarbonsMolecules

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Golub, Pavlo [Czech Academy of Sciences, Prague (Czech Republic)] (ORCID:0000000308344281), Yang, Chao [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)] (ORCID:0000000171727539), Vlček, Vojtěch [University of California, Santa Barbara, CA (United States)] (ORCID:0000000228367619), Veis, Libor [Czech Academy of Sciences, Prague (Czech Republic)] (ORCID:0000000242296335). 2025-03-24. Quantum Chemical Density Matrix Renormalization Group Method Boosted by Machine Learning. https://doi.org/10.1021/acs.jpclett.5c00207

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