Molecular Hamiltonians and overlap matrices -------------------------------------------- These datasets contain structures, energies, forces, Hamiltonians (Fock or Kohn-Sham matrices) and overlap matrices for water, ethanol, malondialdehyde and uracil used for training the SchNOrb model. The datasets use Hartree as units of energy and Angstrom as units of length. The overlap matrices are dimensionless. All calculations with the exception of the schnorb_hamiltonian_ethanol_hf.tgz dataset were carried out at the PBE/def2-SVP level of theory [1,2] using the ORCA quantum chemistry package (4.0.1.2).[3] The latter was computed at the HF/def2-SVP level. For details on how the individual datasets were generated please refer to the publication. Format ------- The datasets are stored as tar archives compressed via gzip and can be unpacked on a Unix machine using the the "tar -xzf .tgz" command. The data itself is stored in form of an atomic simulation environment (ASE) sqlite database[4]. The database can be accessed using the standard tools provided by ASE. In addition, a utility class (schnetpack.data.AtomsData) for loading the datasets can be found in the SchNetPack code package for atomistic machine learning [5]. Information on the names of the properties, units and the reference energies of the free atoms, as well as further details on the settings used for the reference computations is provided in the database metadata, which can be inspected via "ase db -m .db". The following example python code can be used to load the data associated with a single entry with the index from a database file: """ from schnetpack.data import AtomsData database = AtomsData("path/to/db") atoms, properties = database.get_properties(idx) """ is an ASE atoms object containing the positions and atom types, while is a dictionary containing all requested properties. How to cite ------------ K. T. Schütt, M. Gastegger, A. Tkatchenko, K. R. Müller, R. J. Maurer Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions. Nature commun., 10(1), 1-10. 2019 References and links --------------------- [1] Perdew, J. P.; Burke, K.; Ernzerhof, M. Phys. Rev. Lett. 77 (18), 3865–3868 (1996). [2] Weigend, F.; Ahlrichs, R. Phys. Chem. Chem. Phys. 7, 3297-3305 (2005). [3] Neese, F. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2, 73-78 (2012). [4] https://wiki.fysik.dtu.dk/ase/index.html [5] https://github.com/atomistic-machine-learning/schnetpack