Omkar2118/datasets huggingface dataset:
NMC Li-ion Battery Cathode Energies and Charge Densities

This dataset contains charge densities for NMC (Ni, Mn and Co) 2x2x1 supercell (12 transition metal atoms and 12 Li/vacancy site) with varying levels of Li content. For each structure we first randomly sample the number of Mn, Ni and Co atoms given that the total number of transition metal atoms is 12 and then randomly assign them to the transition metal positions of the lattice. Similarly the number of vacancies is uniformly sampled between 0 and 12 and vacancies are assigned to the Li site. The generated configurations are then relaxed in two steps: First we relax the atom positions with fixed cell parameters and then we allow both positions and cell parameters to relax. We keep only the electron density (CHGCAR) file after the last cell relaxation step. The atoms are relaxed until forces on each atom are lower than 0.01 eV/Å.

The final relaxation is done with the following VASP settings (DFT level of theory) through the Atomic Simulation Environment:

xc='PBE', gga='PS', istart=1, algo='Normal', icharg=1, nelm=1800, ispin=1, nelmdl=6, isym=0, lcorr=True, potim=0.1, nelmin=5, kpts=[3,3,1], ismear=0, ediff=0.1E-03, ediffg=-0.05, sigma=0.1, nsw=200, isif=3, ibrion=2, ldiag=True, lreal='Auto', lwave=False, lcharg=True, prec='Normal'

The resulting CHGCAR files have been compressed with lz4 compression and packed in non-compressed tar archives with up to 1000 structures in each.

The datasplits json files contain the indices (0-index) of the train, validation and test sets used in the paper "Equivariant Graph neural networks for fast electron density estimation of molecules, liquids, and solids"

Introducing paper: Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
Ethylene Carbonate Molecular Dynamics

This dataset consists of charge densities of individual snapshots from a molecular dynamics trajectory (DFT simulations?). We insert 8 ethylene carbonate molecules in the simulation box. To quickly explore a large part of the configurational space we put Hookean constraints on the molecular bonds (to maintain molecular identity such that molecules are not torn apart at such high temperature) and run Langevin molecular dynamics with thermostat temperature of 3000 K. The simulation was run for 12380 steps of 0.5 fs.

The resulting CHGCAR files have been compressed with lz4 compression and packed in non-compressed tar archives with up to 1000 structures in each.

The datasplits json files contain the indices (0-index) of the train, validation and test sets used in the paper "Equivariant Graph neural networks for fast electron density estimation of molecules, liquids, and solids"

To only use equilibrated structures the first 1000 time steps are not used in the paper. Index 0 in the split files corresponds to index 1000 of the CHGCAR files (01000.CHGCAR.lz4).

Introducing paper: Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids