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

Data associated with the paper entitled

On application of Deep Learning to simplified quantum-classical dynamics in electronically excited states

Three TD-DFTB datasets (sX_10_force.db) 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 (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/Å for forces). The file format is SQLite as provided by the ASE;

Three pre-trained Deep Learning models (best_model_sX) 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;

500_init_conditions.tar.gz  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.