FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2903235585> ?p ?o ?g. }

• W2903235585 endingPage "204106" @default.
• W2903235585 startingPage "204106" @default.
• W2903235585 abstract "A new diabatization method based on artificial neural networks (ANNs) is presented, which is capable of reproducing high-quality ab initio data with excellent accuracy for use in quantum dynamics studies. The diabatic potential matrix is expanded in terms of a set of basic coupling matrices and the expansion coefficients are made geometry-dependent by the output neurons of the ANN. The ANN is trained with respect to ab initio data using a modified Marquardt-Levenberg back-propagation algorithm. Due to its setup, this approach combines the stability and straightforwardness of a standard low-order vibronic coupling model with the accuracy by the ANN, making it particularly advantageous for problems with a complicated electronic structure. This approach combines the stability and straightforwardness of a standard low-order vibronic coupling model with the accuracy by the ANN, making it particularly advantageous for problems with a complicated electronic structure. This novel ANN diabatization approach has been applied to the low-lying electronic states of NO3 as a prototypical and notoriously difficult Jahn-Teller system in which the accurate description of the very strong non-adiabatic coupling is of paramount importance. Thorough tests show that an ANN with a single hidden layer is sufficient to achieve excellent results and the use of a deeper layering shows no clear benefit. The newly developed diabatic ANN potential energy surface (PES) model accurately reproduces a set of more than 90 000 Multi-configuration Reference Singles and Doubles Configuration Interaction (MR-SDCI) energies for the five lowest PES sheets." @default.
• W2903235585 created "2018-12-11" @default.
• W2903235585 creator A5007746845 @default.
• W2903235585 creator A5015375055 @default.
• W2903235585 date "2018-11-28" @default.
• W2903235585 modified "2023-10-15" @default.
• W2903235585 title "Neural network diabatization: A new <i>ansatz</i> for accurate high-dimensional coupled potential energy surfaces" @default.
• W2903235585 cites W1517218431 @default.
• W2903235585 cites W1580066453 @default.
• W2903235585 cites W1963760345 @default.
• W2903235585 cites W1964882117 @default.
• W2903235585 cites W1966939946 @default.
• W2903235585 cites W1967725011 @default.
• W2903235585 cites W1971920777 @default.
• W2903235585 cites W1975997599 @default.
• W2903235585 cites W1976672535 @default.
• W2903235585 cites W1985542939 @default.
• W2903235585 cites W1988996307 @default.
• W2903235585 cites W1992527220 @default.
• W2903235585 cites W1995308330 @default.
• W2903235585 cites W1997099468 @default.
• W2903235585 cites W1998126383 @default.
• W2903235585 cites W1998860313 @default.
• W2903235585 cites W2002110641 @default.
• W2903235585 cites W2006237002 @default.
• W2903235585 cites W2007168050 @default.
• W2903235585 cites W2007388110 @default.
• W2903235585 cites W2008216290 @default.
• W2903235585 cites W2009406001 @default.
• W2903235585 cites W2015269731 @default.
• W2903235585 cites W2015541791 @default.
• W2903235585 cites W2015941622 @default.
• W2903235585 cites W2018065950 @default.
• W2903235585 cites W2020256135 @default.
• W2903235585 cites W2021799365 @default.
• W2903235585 cites W2021891874 @default.
• W2903235585 cites W2022648984 @default.
• W2903235585 cites W2025357349 @default.
• W2903235585 cites W2025444507 @default.
• W2903235585 cites W2026142881 @default.
• W2903235585 cites W2027049592 @default.
• W2903235585 cites W2029188935 @default.
• W2903235585 cites W2030280844 @default.
• W2903235585 cites W2031792806 @default.
• W2903235585 cites W2037273319 @default.
• W2903235585 cites W2039790085 @default.
• W2903235585 cites W2040935428 @default.
• W2903235585 cites W2042340977 @default.
• W2903235585 cites W2042810961 @default.
• W2903235585 cites W2043453635 @default.
• W2903235585 cites W2048359797 @default.
• W2903235585 cites W2055468654 @default.
• W2903235585 cites W2058408365 @default.
• W2903235585 cites W2061179540 @default.
• W2903235585 cites W2067050455 @default.
• W2903235585 cites W2068473123 @default.
• W2903235585 cites W2071020586 @default.
• W2903235585 cites W2075964217 @default.
• W2903235585 cites W2078747826 @default.
• W2903235585 cites W2078824364 @default.
• W2903235585 cites W2082369519 @default.
• W2903235585 cites W2084276247 @default.
• W2903235585 cites W2087875621 @default.
• W2903235585 cites W2089868679 @default.
• W2903235585 cites W2090315303 @default.
• W2903235585 cites W2090622080 @default.
• W2903235585 cites W2094560786 @default.
• W2903235585 cites W2102160853 @default.
• W2903235585 cites W2103496339 @default.
• W2903235585 cites W2128531934 @default.
• W2903235585 cites W2130437470 @default.
• W2903235585 cites W2145319718 @default.
• W2903235585 cites W2155482699 @default.
• W2903235585 cites W2318118870 @default.
• W2903235585 cites W2469253416 @default.
• W2903235585 cites W2491861284 @default.
• W2903235585 cites W2556976958 @default.
• W2903235585 cites W2570669798 @default.
• W2903235585 cites W2752773318 @default.
• W2903235585 cites W2773749023 @default.
• W2903235585 cites W2783454837 @default.
• W2903235585 doi "https://doi.org/10.1063/1.5053664" @default.
• W2903235585 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30501255" @default.
• W2903235585 hasPublicationYear "2018" @default.
• W2903235585 type Work @default.
• W2903235585 sameAs 2903235585 @default.
• W2903235585 citedByCount "43" @default.
• W2903235585 countsByYear W29032355852019 @default.
• W2903235585 countsByYear W29032355852020 @default.
• W2903235585 countsByYear W29032355852021 @default.
• W2903235585 countsByYear W29032355852022 @default.
• W2903235585 countsByYear W29032355852023 @default.
• W2903235585 crossrefType "journal-article" @default.
• W2903235585 hasAuthorship W2903235585A5007746845 @default.
• W2903235585 hasAuthorship W2903235585A5015375055 @default.
• W2903235585 hasBestOaLocation W29032355852 @default.
• W2903235585 hasConcept C109663097 @default.
• W2903235585 hasConcept C112972136 @default.

• next