AIRI-Institute / nablaDFT

nablaDFT: Large-Scale Conformational Energy and Hamiltonian Prediction benchmark and dataset
https://doi.org/10.1039/D2CP03966D
MIT License
159 stars 16 forks source link

Forces and coefficients_matrix are zero #13

Closed KonstantinUshenin closed 7 months ago

KonstantinUshenin commented 10 months ago

I downloaded dataset dataset_train_2k.db. It weight is 7.4 Gb, bash sum is 00462 7256216, and bash md5sum is b9fe99dca36e3b8bddf7ca4bb4c69eae.

I have tried to use dataset like this:

train = HamiltonianDatabase("database/dataset_train_2k.db")

atoms_numbers, \
atoms_positions, \
energy, \
forces, \
core_hamiltonian, \
overlap_matrix, \
coefficients_matrix = train[100]

print(forces)

All forces are zero: [[0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.]]

coefficients_matrix is also zero. Is this a correct values?

KuzmaKhrabrov commented 10 months ago

Yes, it is correct for the current version of nablaDFT dataset