Two Excited-state Datasets for Quantum Chemical UV-vis Spectra of Organic Molecules

Overview

Journal Sci Data

Specialty Science

Date 2023 Aug 21

PMID 37604820

Authors

Massimiliano Lupo Pasini

Kshitij Mehta

Pilsun Yoo

Stephan Irle

Affiliations

Soon will be listed here.

Abstract

We present two open-source datasets that provide time-dependent density-functional tight-binding (TD-DFTB) electronic excitation spectra of organic molecules. These datasets represent predictions of UV-vis absorption spectra performed on optimized geometries of the molecules in their electronic ground state. The GDB-9-Ex dataset contains a subset of 96,766 organic molecules from the original open-source GDB-9 dataset. The ORNL_AISD-Ex dataset consists of 10,502,904 organic molecules that contain between 5 and 71 non-hydrogen atoms. The data reveals the close correlation between the magnitude of the gaps between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO), and the excitation energy of the lowest singlet excited state energies quantitatively. The chemical variability of the large number of molecules was examined with a topological fingerprint estimation based on extended-connectivity fingerprints (ECFPs) followed by uniform manifold approximation and projection (UMAP) for dimension reduction. Both datasets were generated using the DFTB+ software on the "Andes" cluster of the Oak Ridge Leadership Computing Facility (OLCF).

Citing Articles

Deep learning workflow for the inverse design of molecules with specific optoelectronic properties.

Yoo P, Bhowmik D, Mehta K, Zhang P, Liu F, Lupo Pasini M Sci Rep. 2023; 13(1):20031.

PMID: 37973879 PMC: 10654498. DOI: 10.1038/s41598-023-45385-9.

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