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4 results for “Inelastic Neutron Scattering”
INSPIRED: Inelastic Neutron Scattering Prediction for Instantaneous Results and Experimental Design
<p>INSPIRED is a graphic user interface (GUI) that performs rapid prediction and calculation of phonons and inelastic neutron scattering (INS) spectra. It consists of three modules. The "Predictor" module uses a symmetry-aware neural network (coupled with an autoencoder) [1-3] to perform direct prediction of total/partial phonon density of states and powder 1D/2D INS spectra from a given structure. The "DFT database" module uses pre-calculated force constants from density functional theory (DFT) [4] to perform INS simulations for single crystals and powders (for the crystals available in the database). The "MLFF" module uses pre-trained universal force fields [8-12] to perform structural optimization, phonon calculation, and INS simulations for single crystals and powders for any given crystal. The predicted/calculated results are saved in CSV files and can be visualized with the GUI. INSPIRED is developed to be a convenient tool for INS experimental planning, steering, and quick data analysis.</p> <p>This repository contains two files as an update to the previous version:</p> <p>1. A tarball file (dftdb.tar.gz) containing the DFT database (currently with 12734 crystals)</p> <p>2. A VirtualBox appliance file (inspired_vm.ova) to run INSPIRED as a virtual machine.</p> <p>The ML model file (model.tar.gz) remains the same and can be obtained from the previous version.</p> <p>Instructions on how to use these files, as well as the rest part of the software, can be found on the <a href="https://github.com/cyqjh/inspired">GitHub page</a>. </p>
Using generative adversarial networks to match experimental and simulated inelastic neutron scattering data
<p>Files uploaded here are related to the paper titled "Using generative adversarial networks to match experimental and simulated inelastic neutron scattering data". Here we investigate how generative adversarial networks can be used to match simulated- and experimental INS data.</p>
A database of synthetic inelastic neutron scattering spectra from molecules and crystals
<p>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/).</p> <p>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. </p> <p>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/).</p> <p>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. </p> <p>A python script (plot_ins.py) to plot the INS data files is provided<br> Usage: plot_ins.py *.csv {-s [1,2] -x [0:100] -y [0:100] -z [0:2.5]}<br> -s : spectrum index, -x/y/z : range to plot </p> <p><br> The manual for the OCLIMAX software used for INS simulations is also provided for reference.</p>
Dataset: Uncovering Obscured Phonon Dynamics from Powder Inelastic Neutron Scattering using Machine Learning
<p>Dataset of simulated and experimental spectra for the manuscript: Uncovering Obscured Phonon Dynamics from Powder Inelastic Neutron Scattering using Machine Learning. </p> <p>The data.zip contains all the simulated spectra and labels.</p> <p>The dataset.zip contains the divided subsets for training, validation and testing purposes, as well as the experimental dataset.</p>
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