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4 results for “TDDFT”
Determination of secondary species in solution through pump-selective transient absorption spectroscopy and explicit-solvent TDDFT
<p>Explicit and implicit solvent TDDFT of alizarin and tautomers of alizarin in methanol. Transient electronic absorption spectroscopy of alizarin in methanol at different pH values. </p>
Energy Decomposition Analysis for excited states: An Extension based on TDDFT
<p>The data contain the test results of the new exc-EDA methods, which include geometry optimisation of the test systems (fluorenone-methanol, quinoline-water, benzene-TCNE and pyridine-water) and the exc-EDA-results with and without TDA and different XC-functionals. In addition, data of exc-EDA calculations for oligomers of pentacene are also included. All calculations were performed with a developer version of AMS.</p>
QM9-XAS database of 56k QM9 small organic molecules labeled with TDDFT X-ray absorption spectra
<p>Database for training graph neural network (GNN) models in <strong>Integrating Explainability into Graph Neural Network Models for the Prediction of X-ray Absorption Spectra, </strong>by Amir Kotobi, Kanishka Singh, Daniel Höche, Sadia Bari, Robert H.Meißner, and Annika Bande.</p> <p><strong>Included:</strong></p> <ul> <li>qm9_Cedge_xas_56k.npz: the TDDFT XAS spectra of 56k structures from the QM9 dataset, were employed to label the graph dataset. The dataset contains two pairs of key/value entries: <strong>spec_stk</strong>, which represents a 2D array containing energies and oscillator strengths of XAS spectra, and <strong>id</strong>, which consists of the indices of QM9 structures. This data was used to create the QM9-XAS graph dataset.</li> <li>qm9xas_orca_output.zip: the raw ORCA output of TDDFT calculations for the 56k QM9-XAS dataset consists of excitation energies, densities, molecular orbitals, and other relevant information. This unprocessed output serves as a source to derive ground truth data for explaining the predictions made by GNNs.</li> <li>qm9xas_spec_train_val.pt: processed graph train/validation dataset of 50k QM9 structures. It is used as input to GNN models for training and validation.</li> <li>qm9xas_spec_test.pt: processed graph test dataset of 6k QM9 structures. It is used to test the performance of trained GNN models.</li> </ul> <p><strong>Notes on the datasets:</strong></p> <ul> <li>The QM9-XAS dataset was created using ORCA electronic structure package [Neese, F., WIREs Computational Molecular Science 2012, 2, 73–78] to calculate carbon K-edge XAS spectra with the time-dependent density functional theory (TDDFT) method [Petersilka, M.; Gossmann, U. J.; Gross, E. K. U., Phys. Rev. Lett. 1996, 76, 1212–1215]</li> <li>The molecular structures of QM9-XAS datasets were sourced from the QM9 database [R. Ramakrishnan, P. O. Dral, M. Rupp, and O. A. Von Lilienfeld, <em>Sci. Data</em> 1, 1 (2014)].</li> </ul> <p><strong>Funding:</strong></p> <p>This<strong> </strong>research was funded by HIDA Trainee Network program, HAICU, Helmholtz AI-4-XAS, DASHH and HEIBRiDS graduate schools. For theoretical calculations and model training, computational resources at DESY and JFZ were used. </p>
DFT and TDDFT-predicted equilibrium structures of bipyridine-annulated perylene tetracarboxylic ester photocatalysts with PdCl2 and PtCl2
<p>Fully relaxed equilibrium structures of <strong>P-Pd</strong> and <strong>P-Pt</strong> as predicted at the DFT and TDDFT levels of theory (B3LYP/def2-SVP) including D3BJ dispersion correction and implicit solvent effects (CH<sub>2</sub>Cl<sub>2</sub>). Both photocatalysts were optimized in singlet (S0) and triplet multiplicity in order to evaluate the Franck-Condon photophysics as well as the prominent triplet species involved in the photphysical and photochemical properties, i.e. triplet intra-ligand (3IL) and triplet metal-to-ligand charge transfer (MLCT) states. The multiplicity is indicated in the filename.</p>
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