This repo contains the code for RevGraphVAMPNet
python train.py --pre-train-epoch 300 --epochs 1000 --batch-size 500 --lr 0.0005
--hidden 16 --num-atoms 42 --num-classes 4 --num_neighbors 10 --conv_type SchNet
--dmin 0 --dmax 8. --step 0.5 --dist-data ../intermediate/red_5nbrs_1ns_dist_min.npy
--nbr-data ../intermediate/red_5nbrs_1ns_inds_min.npy
--data-info ../intermediate/red_5nbrs_1ns_datainfo_min.npy --residual
--train --score-method VAMPCE --save-folder ab_sch4_1
python train.py --pre-train-epoch 300 --epochs 1000 --batch-size 500 --lr 0.0005
--hidden 16 --num-atoms 42 --num-classes 4 --num_neighbors 10 --conv_type SchNet
--dmin 0 --dmax 8. --step 0.5 --dist-data ../intermediate/red_5nbrs_1ns_dist_min.npy
--nbr-data ../intermediate/red_5nbrs_1ns_inds_min.npy
--data-info ../intermediate/red_5nbrs_1ns_datainfo_min.npy
--residual --score-method VAMPCE --return-emb --return-attn --score-method VAMPCE
--save-folder abred_all_1 --trained-model ab_sch4_1/best_allnet.pt
- pytorch
- deeptime
- torch_scatter
- VAMPNet code is based on deeptime package deeptime
- GraphNet code is based on the GraphVampNet
- SchNet code is based on the cgnet
- physical constraint code is based on the RevNet
If you use this code please cite the following paper:
Ying Huang ...