A database of synthetic inelastic neutron scattering spectra from molecules and crystals
Contributors
Other (2):
- 1. Oak Ridge National Laboratory
Description
This database contains simulated inelastic neutron scattering (INS) spectra for 10,000+ inorganic crystals and 20,000+ organic molecules. The INS database for inorganic crystals is based on the phonon database at Kyoto University by Atsushi Togo (http://phonondb.mtl.kyoto-u.ac.jp/). The INS database for organic molecules is based on the QM8 dataset (http://quantum-machine.org/datasets/).
Entry lists can be found in crystals.dat and molecules.dat. After unzipping the tar.gz files, data for each structure model can be found in a subfolder.
For the inorganic crystal database, each subfolder contains five files: a structure.cif file for the crystal structure, a vis_inc_0K.csv file containing the simulated VISION/TOSCA spectra, a powder_2Dmesh_coh_0K.csv file containing the simulated powder S(Q,E), a vis_nwdos.csv file containing the neutron weighted PDOS, a vis_dos.csv file containing the true PDOS, and a gamma_modes.xyz file containing the displacements of gamma point phonons for visualization (with Jmol, http://jmol.sourceforge.net/).
For the QM8 molecular database, there are five files in each subfolder: an INFO-* file containing the SMILES string as well as the IUPAC name (if available) for this molecule, a *.com file containing the input for Gaussian simulation (which also contains the atomic coordinates), a *vis_inc_0K.csv file containing the simulated INS spectra, a *.xyz file containing the atomic displacement of each vibrational modes (can be visualized with Jmol), and a *modes.csv file containing the calculated INS intensity for each normal mode.
A python script (plot_ins.py) to plot the INS data files is provided
Usage: plot_ins.py *.csv {-s [1,2] -x [0:100] -y [0:100] -z [0:2.5]}
-s : spectrum index, -x/y/z : range to plot
Notes
Files
readme.txt
Files
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Additional details
References
- Phonon database at Kyoto University by Atsushi Togo http://phonondb.mtl.kyoto-u.ac.jp/
- Jain, A. et al. Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. APL Materials 1, 011002, doi:10.1063/1.4812323 (2013).
- Ong, S. P. et al. Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis. Computational Materials Science 68, 314-319, doi:https://doi.org/10.1016/j.commatsci.2012.10.028 (2013).
- QM8 dataset at http://quantum-machine.org/datasets/
- Ruddigkeit, L., van Deursen, R., Blum, L. C. & Reymond, J.-L. Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17. Journal of Chemical Information and Modeling 52, 2864-2875, doi:10.1021/ci300415d (2012).
- Ramakrishnan, R., Hartmann, M., Tapavicza, E. & von Lilienfeld, O. A. Electronic spectra from TDDFT and machine learning in chemical space. The Journal of Chemical Physics 143, 084111, doi:10.1063/1.4928757 (2015).
- Frisch, M. et al. Gaussian 09, Revision E. 01, 2013, Gaussian. Inc.: Wallingford CT.
- Togo, A. & Tanaka, I. First principles phonon calculations in materials science. Scripta Materialia 108, 1-5, doi:https://doi.org/10.1016/j.scriptamat.2015.07.021 (2015).
- Cheng, Y. Q., Daemen, L. L., Kolesnikov, A. I. & Ramirez-Cuesta, A. J. Simulation of Inelastic Neutron Scattering Spectra Using OCLIMAX. Journal of Chemical Theory and Computation 15, 1974-1982, doi:10.1021/acs.jctc.8b01250 (2019).