Phenanthrene: TD-DFTB datasets, pre-trained SchNet models and initial coniditions for TSH

<p><em>Data associated with the paper entitled </em></p> <p><strong>On application of Deep Learning to simplified quantum-classical dynamics in electronically excited states</strong></p> <ol> <li>Three TD-DFTB datasets&nbsp;(<strong>sX_10_force.db</strong>) have been produced using the <a href="https://wiki.fysik.dtu.dk/ase/">Atomic Simulation Environment</a> (ASE) coupled to <a href="http://demon-nano.ups-tlse.fr/">deMon-Nano</a> code for the linear response Time-Dependent Density Functional based Tight-Binding (TD-DFTB) calculations. Each dataset contains 10000 TD-DFTB electronic structure calculations for a given excited singlet state (S₂/S₃/S₄) of a neutral phenanthrene molecule. Each database entry contains Cartesian atomic coordinates as well as potential energy and atomic forces for a given excited state at a given geometry. Since ASE has been used, all physical quantities are stored in the corresponding units (e.g. eV for energy or eV/&Aring; for forces). The file format is SQLite as provided by the ASE;</li> <li>Three pre-trained Deep Learning models (<strong>best_model_sX</strong>) for a given excited singlet state have been produced using <a href="https://schnetpack.readthedocs.io/en/stable/">SchNetPack</a> package, which implements the SchNet architecture for atomistic simulations. Each model has been trained using the corresponding TD-DFTB dataset from #1. The file format is binary as provided by the SchNetPack;</li> <li><a href="https://zenodo.org/api/files/f1925cb5-66a8-4c6f-809b-3414f0cbc1d5/500_init_conditions.tar.gz"><strong>500_init_conditions.tar.gz</strong>&nbsp;</a> contains 500 initial conditions (Cartesian coordinates and velocities), which can be used for Trajectory Surface Hopping (TSH) simulations with or without the pre-trained models from #2.</li> </ol> <p>&nbsp;</p>