Dataset for "Solving deep-learning density-functional theory via variational autoencoder"
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https://zenodo.org/record/10814855
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资源简介：
The dataset contains the ground state energies, the ground state density profiles, and the external potentials of a 3D single particle system with a Gaussian-like external potential.The number of grid points for each dimension is \(N_g=18\), the linear length of the box is \(L=a_0\) with \(a_0\) the unit of length. The unit of energy is \(E_0=\frac{ \hbar^2}{(m a_0^2)}\).
The dataset is zip file of a Python npz file with the following keys:
- "density" that corresponds to the ground state density profile.- "potential" is the external potential.-"energy" is the ground state energy.
The number of instances is 36000.
-3D_gaussian.zip -> 3D_gaussian.npz
a dictionary with three keys -density, potential, energy-. The dimension of both potential and density is \([N_d,N_g,N_g,N_g]\). The shape of energy is \([N_d]\). \(N_d=36000\)
-3D_gaussian_transfer_test_1.npz
a dictionary with three keys -density, potential, energy-. The dimension of both potential and density is \([N_d,N_g,N_g,N_g]\). The shape of energy is \([N_d]\). \(N_d=500\)
-3D_gaussian_transfer_test_2.npz
a dictionary with three keys -density, potential, energy-. The dimension of both potential and density is \([N_d,N_g,N_g,N_g]\). The shape of energy is \([N_d]\). \(N_d=500\)
创建时间：
2024-05-20
