QM9-XAS database of 56k QM9 small organic molecules labeled with TDDFT X-ray absorption spectra ...

• Main Author: Kotobi, Amir
• Format: Dataset
• Language: unknown
• Published: Zenodo 2023
• Subjects: QM9-XAS; Graph Neural Networks; Machine Learning; Explainability Artificial Intelligence; Orca
• Online Access: https://dx.doi.org/10.5281/zenodo.8276902; https://zenodo.org/record/8276902

Database for training graph neural network (GNN) models in Integrating Explainability into Graph Neural Network Models for the Prediction of X-ray Absorption Spectra, by Amir Kotobi, Kanishka Singh, Daniel Höche, Sadia Bari, Robert H.Meißner, and Annika Bande. Included: 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: spec_stk , which represents a 2D array containing energies and oscillator strengths of XAS spectra, and id , which consists of the indices of QM9 structures. This data was used to create the QM9-XAS graph dataset. 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. qm9xas_spec_train_val.pt: processed graph ... : {"references": ["Neese, F.,\u00a0WIREs Computational Molecular Science 2012, 2, 73\u201378", "Petersilka, M.; Gossmann, U. J.; Gross, E. K. U.,\u00a0Phys. Rev. Lett. 1996, 76, 1212\u20131215", "R. Ramakrishnan, P. O. Dral, M. Rupp, and O. A. Von Lilienfeld,\u00a0Sci. Data, 2014,\u00a01, 1"]} ...