Comments (3)
Hello, would you mind sharing the code to generate this figure and video? We plan to release this new dynamic dataset soon, and plan to test these two.
from airs.
Hello, we have uploaded the new MD datasets. The new MD is conducted with 5 a.u. (https://en.wikipedia.org/wiki/Atomic_units) (~0.12 fs) with 1000 steps. Then, we sample 100 steps evenly (timestep 50 a.u.) to get the QH9Dynamic datasets. This is conducted in Pyscf 2.3.0. The full dataset will be provided upon request.
from airs.
Hi, I add a jupyter to show the MAE on the Hamiltonian matrix for the MD trajectory in current dataset. Meanwhile, the dataset generation example code is released here. Thanks.
from airs.
Related Issues (14)
- Strange Results
- PotNet enviroment set up HOT 8
- Trained weights Latent diffusion for protein structure generation HOT 1
- the training loss of QHNet did not drop HOT 4
- QH9: Reproducing Hamiltonians with PySCF gives 2[μHa] error. HOT 3
- How can I train protein_autoencoder/main.py using multiple GPUs? HOT 2
- How can I get the score_norm.txt for a new dataset in the SyMat
- nothing
- A question about irreps in QHNet HOT 1
- SMILES of QH9 entries HOT 2
- Jarvis version update HOT 1
- Request for Dataset Division Files: Following Your Methodology HOT 1
- wrong convention for PySCF format data
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from airs.