FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2894916987> ?p ?o ?g. }

• W2894916987 endingPage "60" @default.
• W2894916987 startingPage "52" @default.
• W2894916987 abstract "Two-dimensional electronic spectroscopy has become one of the main experimental tools for analyzing the dynamics of excitonic energy transfer in large molecular complexes. Simplified theoretical models are usually employed to extract model parameters from the experimental spectral data. Here we show that computationally expensive but exact theoretical methods encoded into a neural network can be used to extract model parameters and infer structural information such as dipole orientation from two dimensional electronic spectra (2DES) or reversely, to produce 2DES from model parameters. We propose to use machine learning as a tool to predict unknown parameters in the models underlying recorded spectra and as a way to encode computationally expensive numerical methods into efficient prediction tools. We showcase the use of a trained neural network to efficiently compute disordered averaged spectra and demonstrate that disorder averaging has non-trivial effects for polarization controlled 2DES." @default.
• W2894916987 created "2018-10-12" @default.
• W2894916987 creator A5015279824 @default.
• W2894916987 creator A5030354174 @default.
• W2894916987 date "2019-04-01" @default.
• W2894916987 modified "2023-10-10" @default.
• W2894916987 title "Machine learning of two-dimensional spectroscopic data" @default.
• W2894916987 cites W1966689114 @default.
• W2894916987 cites W1999041491 @default.
• W2894916987 cites W2013398650 @default.
• W2894916987 cites W2023813727 @default.
• W2894916987 cites W2026354169 @default.
• W2894916987 cites W2035838814 @default.
• W2894916987 cites W2058468006 @default.
• W2894916987 cites W2060234842 @default.
• W2894916987 cites W2064675550 @default.
• W2894916987 cites W2088307177 @default.
• W2894916987 cites W2090062662 @default.
• W2894916987 cites W2104489082 @default.
• W2894916987 cites W2112796928 @default.
• W2894916987 cites W2136922672 @default.
• W2894916987 cites W2137983211 @default.
• W2894916987 cites W2139840851 @default.
• W2894916987 cites W2141045244 @default.
• W2894916987 cites W2141703670 @default.
• W2894916987 cites W2145838318 @default.
• W2894916987 cites W2229940530 @default.
• W2894916987 cites W2334149794 @default.
• W2894916987 cites W2336855371 @default.
• W2894916987 cites W2341702711 @default.
• W2894916987 cites W2419175238 @default.
• W2894916987 cites W2531046554 @default.
• W2894916987 cites W2599873442 @default.
• W2894916987 cites W2737127163 @default.
• W2894916987 cites W2793259542 @default.
• W2894916987 cites W2803260581 @default.
• W2894916987 cites W2804534705 @default.
• W2894916987 cites W2886022695 @default.
• W2894916987 cites W2889632381 @default.
• W2894916987 cites W3102449990 @default.
• W2894916987 cites W3106306262 @default.
• W2894916987 doi "https://doi.org/10.1016/j.chemphys.2019.01.002" @default.
• W2894916987 hasPublicationYear "2019" @default.
• W2894916987 type Work @default.
• W2894916987 sameAs 2894916987 @default.
• W2894916987 citedByCount "24" @default.
• W2894916987 countsByYear W28949169872019 @default.
• W2894916987 countsByYear W28949169872020 @default.
• W2894916987 countsByYear W28949169872021 @default.
• W2894916987 countsByYear W28949169872022 @default.
• W2894916987 countsByYear W28949169872023 @default.
• W2894916987 crossrefType "journal-article" @default.
• W2894916987 hasAuthorship W2894916987A5015279824 @default.
• W2894916987 hasAuthorship W2894916987A5030354174 @default.
• W2894916987 hasBestOaLocation W28949169872 @default.
• W2894916987 hasConcept C104317684 @default.
• W2894916987 hasConcept C105795698 @default.
• W2894916987 hasConcept C11413529 @default.
• W2894916987 hasConcept C121332964 @default.
• W2894916987 hasConcept C121864883 @default.
• W2894916987 hasConcept C147789679 @default.
• W2894916987 hasConcept C154945302 @default.
• W2894916987 hasConcept C16345878 @default.
• W2894916987 hasConcept C173523689 @default.
• W2894916987 hasConcept C178790620 @default.
• W2894916987 hasConcept C185592680 @default.
• W2894916987 hasConcept C186060115 @default.
• W2894916987 hasConcept C205049153 @default.
• W2894916987 hasConcept C2524010 @default.
• W2894916987 hasConcept C30475298 @default.
• W2894916987 hasConcept C32891209 @default.
• W2894916987 hasConcept C33923547 @default.
• W2894916987 hasConcept C41008148 @default.
• W2894916987 hasConcept C4839761 @default.
• W2894916987 hasConcept C49874194 @default.
• W2894916987 hasConcept C50644808 @default.
• W2894916987 hasConcept C55037315 @default.
• W2894916987 hasConcept C55493867 @default.
• W2894916987 hasConcept C62520636 @default.
• W2894916987 hasConcept C66746571 @default.
• W2894916987 hasConcept C86803240 @default.
• W2894916987 hasConceptScore W2894916987C104317684 @default.
• W2894916987 hasConceptScore W2894916987C105795698 @default.
• W2894916987 hasConceptScore W2894916987C11413529 @default.
• W2894916987 hasConceptScore W2894916987C121332964 @default.
• W2894916987 hasConceptScore W2894916987C121864883 @default.
• W2894916987 hasConceptScore W2894916987C147789679 @default.
• W2894916987 hasConceptScore W2894916987C154945302 @default.
• W2894916987 hasConceptScore W2894916987C16345878 @default.
• W2894916987 hasConceptScore W2894916987C173523689 @default.
• W2894916987 hasConceptScore W2894916987C178790620 @default.
• W2894916987 hasConceptScore W2894916987C185592680 @default.
• W2894916987 hasConceptScore W2894916987C186060115 @default.
• W2894916987 hasConceptScore W2894916987C205049153 @default.
• W2894916987 hasConceptScore W2894916987C2524010 @default.
• W2894916987 hasConceptScore W2894916987C30475298 @default.
• W2894916987 hasConceptScore W2894916987C32891209 @default.
• W2894916987 hasConceptScore W2894916987C33923547 @default.

• next