Phenanthrene: TD-DFTB datasets, pre-trained SchNet models and initial coniditions for TSH
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<em>Data associated with the paper entitled </em> <strong>On application of Deep Learning to simplified quantum-classical dynamics in electronically excited states</strong> Three TD-DFTB datasets (<strong>sX_10_force.db</strong>) have been produced using the Atomic Simulation Environment (ASE) coupled to deMon-Nano 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 (S<sub>2</sub>/S<sub>3</sub>/S<sub>4</sub>) 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/Å for forces). The file format is SQLite as provided by the ASE; Three pre-trained Deep Learning models (<strong>best_model_sX</strong>) for a given excited singlet state have been produced using SchNetPack 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; <strong>500_init_conditions.tar.gz</strong> 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.
