Quantum Chemical Density Matrix Renormalization Group Method Boosted by Machine Learning

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61388955%3A_____%2F25%3A00618676" target="_blank" >RIV/61388955:_____/25:00618676 - isvavai.cz</a>
Result on the web
<a href="https://hdl.handle.net/11104/0365504" target="_blank" >https://hdl.handle.net/11104/0365504</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1021/acs.jpclett.5c00207" target="_blank" >10.1021/acs.jpclett.5c00207</a>

Result language
angličtina
Original language name
Quantum Chemical Density Matrix Renormalization Group Method Boosted by Machine Learning
Original language description
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 Delta-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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10403 - Physical chemistry

Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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