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Supplementary Material for GDB-9-Ex

    Pilsun Yoo | Oak Ridge National Laboratory
    Massimiliano Lupo Pasini | Oak Ridge National Laboratory
    Kshitij Mehta | Oak Ridge National Laboratory
    Stephan Irle | Oak Ridge National Laboratory
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Description

This dataset provides supplementary material for the previously published dataset GDB-9-Ex (1), which is available at the following website: https://www.osti.gov/dataexplorer/biblio/dataset/1890227 The dataset contains a file called gdb9_ex.csv that synthesizes the information contained in GDB-9-Ex. Each row in a .CSV file is associated with a molecule, and the columns contain the following information: 1) molecules ID 2) SMILES string representation 3) DFTB-PE (eV): formation energy 4) first 50 electronic excitation modes 5) oscillators strengths of the first 50 electronic excitation modes The compression of this information into CSV files will allow a more agile extraction and management of information to the users that do not have access to large scale HPC platforms. REFERENCES (1) Lupo Pasini, Massimiliano, Yoo, Pilsun, Mehta, Kshitij, and Irle, Stephan. GDB-9-Ex: Quantum chemical prediction of UV/Vis absorption spectra for GDB-9 molecules. United States: N. p., 2022. Web. doi:10.13139/OLCF/1890227.

Funding Information

DOE Contract Number

DE-AC05-00OR22725

Originating Research Organization

Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization

Office of Science (SC)

Related Works

Details

Release Date

June 28, 2023

Subject

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY, 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS, 74 ATOMIC AND MOLECULAR PHYSICS, 97 MATHEMATICS AND COMPUTING

Keywords

Time-dependent density-functional tight-binding (TD-DFTB), Predicting Excited States Molecular Properties

Dataset

Dataset Type

ND Numeric Data

Software

Python

Cite This Dataset:

Yoo, P., Lupo Pasini, M., Mehta, K., Irle, S. (2023). Supplementary Material for GDB-9-Ex. Oak Ridge National Laboratory. https://doi.org/10.13139/OLCF/1985521.

Acknowledgements

This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Advanced Scientific Computing Research programs in the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.