Molecular Geometries and Energies for "Reorganization Energy Predictions with Graph Neural Networks Informed by Low-Cost Conformers"

Data and implementation of ChIRo and SchNet for "Accelerating Organic Electronic Materials Design with Low-Cost Molecular Reorganization Energy Predictions"Contains molecular geometries, electron affinities, and ionization energies for a dataset used to train organic molecule reorganization energies in the following paper:<i>J. Phys. Chem. A</i> 2023, 127, 15, 3484–3489https://doi.org/10.1021/acs.jpca.2c09030