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Electronic Descriptors for Supervised Spectroscopic Predictions
[Image: see text] 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 b...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10061681/ https://www.ncbi.nlm.nih.gov/pubmed/36877528 http://dx.doi.org/10.1021/acs.jctc.2c01039 |
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author | de Armas-Morejón, Carlos Manuel Montero-Cabrera, Luis A. Rubio, Angel Jornet-Somoza, Joaquim |
author_facet | de Armas-Morejón, Carlos Manuel Montero-Cabrera, Luis A. Rubio, Angel Jornet-Somoza, Joaquim |
author_sort | de Armas-Morejón, Carlos Manuel |
collection | PubMed |
description | [Image: see text] 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, 084111.26328822Ghosh 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, 084111.26328822] Inspired by the TDDFT theory, we propose to use a set of electronic descriptors obtained from low-cost DFT methods: orbital energy differences (Δϵ(ia) = ϵ(a) – ϵ(i)), transition dipole moment between occupied and unoccupied Kohn–Sham orbitals (⟨ϕ(i)|r|ϕ(a)⟩), and when relevant, charge-transfer character of monoexcitations (R(ia)). 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). |
format | Online Article Text |
id | pubmed-10061681 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-100616812023-03-31 Electronic Descriptors for Supervised Spectroscopic Predictions de Armas-Morejón, Carlos Manuel Montero-Cabrera, Luis A. Rubio, Angel Jornet-Somoza, Joaquim J Chem Theory Comput [Image: see text] 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, 084111.26328822Ghosh 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, 084111.26328822] Inspired by the TDDFT theory, we propose to use a set of electronic descriptors obtained from low-cost DFT methods: orbital energy differences (Δϵ(ia) = ϵ(a) – ϵ(i)), transition dipole moment between occupied and unoccupied Kohn–Sham orbitals (⟨ϕ(i)|r|ϕ(a)⟩), and when relevant, charge-transfer character of monoexcitations (R(ia)). 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). American Chemical Society 2023-03-06 /pmc/articles/PMC10061681/ /pubmed/36877528 http://dx.doi.org/10.1021/acs.jctc.2c01039 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | de Armas-Morejón, Carlos Manuel Montero-Cabrera, Luis A. Rubio, Angel Jornet-Somoza, Joaquim Electronic Descriptors for Supervised Spectroscopic Predictions |
title | Electronic Descriptors
for Supervised Spectroscopic
Predictions |
title_full | Electronic Descriptors
for Supervised Spectroscopic
Predictions |
title_fullStr | Electronic Descriptors
for Supervised Spectroscopic
Predictions |
title_full_unstemmed | Electronic Descriptors
for Supervised Spectroscopic
Predictions |
title_short | Electronic Descriptors
for Supervised Spectroscopic
Predictions |
title_sort | electronic descriptors
for supervised spectroscopic
predictions |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10061681/ https://www.ncbi.nlm.nih.gov/pubmed/36877528 http://dx.doi.org/10.1021/acs.jctc.2c01039 |
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