On August 9, 2022, Chen, Zekun; Bononi, Fernanda C.; Sievers, Charles A.; Kong, Wang-Yeuk; Donadio, Davide published an article.HPLC of Formula: 91-16-7 The title of the article was UV-Visible Absorption Spectra of Solvated Molecules by Quantum Chemical Machine Learning. And the article contained the following:
Predicting UV-visible absorption spectra is essential to understand photochem. processes and design energy materials. Quantum chem. methods can deliver accurate calculations of UV-visible absorption spectra, but they are computationally expensive, especially for large systems or when one computes line shapes from thermal averages Here, we present an approach to predict UV-visible absorption spectra of solvated aromatic mols. by quantum chem. (QC) and machine learning (ML). We show that a ML model, trained on the high-level QC calculation of the excitation energy of a set of aromatic mols., can accurately predict the line shape of the lowest-energy UV-visible absorption band of several related mols. with less than 0.1 eV deviation with respect to reference exptl. spectra. Applying linear decomposition anal. on the excitation energies, we unveil that our ML models probe vertical excitations of these aromatic mols. primarily by learning the at. environment of their Ph rings, which align with the phys. origin of the è?髿ªâ? electronic transition. Our study provides an effective workflow that combines ML with quantum chem. methods to accelerate the calculations of UV-visible absorption spectra for various mol. systems. The experimental process involved the reaction of 1,2-Dimethoxybenzene(cas: 91-16-7).HPLC of Formula: 91-16-7
The Article related to aromatic mol uv visible absorption spectra quantum chem learning, Optical, Electron, and Mass Spectroscopy and Other Related Properties: Electronic Spectroscopy and other aspects.HPLC of Formula: 91-16-7
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