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  Electronic Descriptors for Supervised Spectroscopic Predictions

de Armas-Morejón, C. M., Montero-Cabrera, L. A., Rubio, A., & Jornet-Somoza, J. (2023). Electronic Descriptors for Supervised Spectroscopic Predictions. Journal of Chemical Theory and Computation, 19(6), 1818-1826. doi:10.1021/acs.jctc.2c01039.

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ct2c01039_si_001.pdf (Ergänzendes Material), 2MB
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Supporting Information: Additional data and NN descriptions
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acs.jctc.2c01039.pdf (Verlagsversion), 3MB
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© The Authors. Published by American Chemical Society

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https://arxiv.org/abs/2205.12074 (Preprint)
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https://doi.org/10.1021/acs.jctc.2c01039 (Verlagsversion)
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Urheber

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 Urheber:
de Armas-Morejón, C. M.1, 2, Autor
Montero-Cabrera, L. A.2, Autor
Rubio, A.1, 3, 4, Autor           
Jornet-Somoza, J.1, 3, 4, Autor           
Affiliations:
1Nano-Bio Spectroscopy Group, Departamento de Polímeros y Materiales Avanzados: Fisica, Química y Tecnología, Universidad del País Vasco UPV/EHU, ou_persistent22              
2Laboratorio de Química Computacional y Teórica, Facultad de Química, Universidad de La Habana, ou_persistent22              
3Theory Group, Theory Department, Max Planck Institute for the Structure and Dynamics of Matter, Max Planck Society, ou_2266715              
4Center for Free-Electron Laser Science, ou_persistent22              

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 Zusammenfassung: Spectroscopic properties of molecules hold great importance for the description of the molecular response under the effect of UV/vis electromagnetic radiation. Computationally expensive ab initio (e.g., MultiConfigurational SCF, Coupled Cluster) or TDDFT methods are commonly used by the quantum chemistry community to compute these properties. In this work, we propose a (supervised) Machine Learning approach to model the absorption spectra of organic molecules. Several supervised ML methods have been tested such as Kernel Ridge Regression (KRR), Multiperceptron Neural Networs (MLP), and Convolutional Neural Networks. [Ramakrishnan et al. J. Chem. Phys. 2015, 143, 084111.Ghosh et al. Adv. Sci. 2019, 6, 1801367.] The use of only geometrical-atomic number descriptors (e.g., Coulomb Matrix) proved to be insufficient for an accurate training. [Ramakrishnan et al. J. Chem. Phys. 2015, 143, 084111.] Inspired by the TDDFT theory, we propose to use a set of electronic descriptors obtained from low-cost DFT methods: orbital energy differences (Δϵia = ϵa – ϵi), transition dipole moment between occupied and unoccupied Kohn–Sham orbitals (⟨ϕi|r|ϕa⟩), and when relevant, charge-transfer character of monoexcitations (Ria). We demonstrate that with these electronic descriptors and the use of Neural Networks we can predict not only a density of excited states but also get a very good estimation of the absorption spectrum and charge-transfer character of the electronic excited states, reaching results close to chemical accuracy (∼2 kcal/mol or ∼0.1 eV).

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Sprache(n): eng - English
 Datum: 2022-10-192023-03-062023-03-28
 Publikationsstatus: Erschienen
 Seiten: 9
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: arXiv: 2205.12074
DOI: 10.1021/acs.jctc.2c01039
 Art des Abschluß: -

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Projektname : This work was supported by the European Research Council (ERC-2015-AdG694097), the Cluster of Excellence ‘CUI: Advanced Imaging of Matter’ of the Deutsche Forschungsgemeinschaft (DFG) - EXC 2056 - project ID 390715994, Grupos Consolidados (IT1249-19), and the SFB925 “Light induced dynamics and control of correlated quantum systems”. We kindly recognize the partial support of the project ID PN223LH010-002 “Inteligencia Artificial Aplicada, Espectroscopía y Bioactividad” of the Cuban Ministry of Science, Technology and Environment as well as the overall support given to L.A.M.C. by the Universidad de La Habana and the Donostia International Physics Center.
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Quelle 1

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Titel: Journal of Chemical Theory and Computation
  Andere : J. Chem. Theory Comput.
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: Washington, D.C. : American Chemical Society
Seiten: - Band / Heft: 19 (6) Artikelnummer: - Start- / Endseite: 1818 - 1826 Identifikator: ISSN: 1549-9618
CoNE: https://pure.mpg.de/cone/journals/resource/111088195283832