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DOE OSTI · 1996925

ORNL_AISD_DL-HLgap

Yoo, Pilsun·Irle, Stephan·Lupo Pasini, Massimiliano·Mehta, Kshitij
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Abstract

This dataset provides supplementary molecular dataset of Deep Learning Workflow for the Inverse Design of Molecules with Specific Optoelectronic Properties. The dataset comprises three main directories such as GDB-9_dataset, Low_HL_Gap_dataset, and High_HL_Gap_dataset which individually has csv files, smiles_txt files, pdb files and xyz files containing information of molecular structures, properties and coordinates generated from deep learning workflow using generative model, surrogate model and DFTB calculation results. GDB-9_dataset contains the molecular data extracted from the original GDB-9 dataset with additional data of DFTB HL gap, surrogate HL gap and molecular property analysis. (the number of atoms, aromaticity and double bond equivalent) Low_HL_Gap_dataset and High_HL_Gap_dataset contains series of dataset for different generations with further split to train and test dataset that were obtained from the iterative workflow described in the manuscript. Additional directory Chemiscope_visualization in Low_HL_Gap_dataset directory contains compressed json files to visualize molecules using chemiscope.org page or application to help readers examine generated molecules.

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRYDensity-Functional Tight-BindingInverse DesignOptical Property of Molecules

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Yoo, Pilsun, Irle, Stephan, Lupo Pasini, Massimiliano, Mehta, Kshitij. 2023-09-01. ORNL_AISD_DL-HLgap. https://doi.org/10.13139/ornlnccs%2F1996925

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