Dataset: Real-time interpretation of neutron vibrational spectra with symmetry-equivariant Hessian matrix prediction

This dataset contains the optimized structures and the corresponding molecular Hessian matrices of selected molecules in QM8, QM9 and PubChem databases. The data is use to train and validate a machine learning model described in the paper titled "Real-time interpretation of neutron vibrational spectra with symmetry-equivariant Hessian matrix prediction". In the tarball file, the three txt files contains the indexes of molecules in the corresponding datasets. The three folders (for QM8, QM9 and PubChem, respectively) contain the structure (xyz) files and the molecular Hessian (npy) files, which can be loaded with numpy module in Python.

The shape of each Hessian matrix, H, is (3n, 3n), where n is number of atoms in the molecule. The multiplier 3 means that each atom has 3 degrees of freesom, x,y and z. The order of the indexes along row and column is organized as 1x, 1y, 1z, 2x, 2y, 2z, 3x, 3y, 3z, ..., till nx, ny, nz. The order from 1 to n corresponds to the order of the atoms in the structure file. The training data could be easily generated from the data provided here with the script provided in the paper (https://github.com/maplewen4/INS_molecule).